Uncommon Adverse Events of Immune Checkpoint Inhibitors in Small Cell Lung Cancer: A Systematic Review of Case Reports.

BACKGROUND: This study aimed to systematically review case reports documenting rare adverse events in patients with small cell lung cancer (SCLC) following the administration of immune checkpoint inhibitors (ICIs). METHODS: A systematic literature review was conducted to identify case reports detailing previously unreported adverse drug reactions to ICIs in patients with SCLC. The scope of the literature reviewed was restricted to case studies on SCLC published up to 31 December 2023. RESULTS: We analyzed twenty-four studies on ICI use for patients with SCLC. There were six reports on atezolizumab, four on durvalumab, and three on adverse events from monotherapy with nivolumab. Reports involving combination treatments were the most frequent, with a total of six, predominantly involving using nivolumab in combination with ipilimumab. Additionally, there was one report each on using pembrolizumab, nofazinilimab, sintilimab, tislelizumab, and toripalimab. We collected detailed information on the clinical course, including patient and disease characteristics, symptoms, treatment for each adverse event, and recovery status. Among the patients included in the case reports, 21 out of 24 (87.5%) had extensive-stage SCLC when initiating ICI therapy, with only 1 patient diagnosed with limited-stage SCLC. Respiratory system adverse events were most common, with seven cases, followed by neurological, endocrinological, and gastroenterological events. Three case reports documented adverse events across multiple systems in a single patient. In most cases, patients showed symptom improvement; however, four studies reported cases where patients either expired without symptom improvement or experienced sequelae. CONCLUSIONS: Efforts to develop reliable biomarkers for predicting irAEs continue, with ongoing research to enhance predictive precision. Immunotherapy presents diverse and unpredictable adverse events, underscoring the need for advanced diagnostic tools and a multidisciplinary approach to improve patient management.

Prediction of breast cancer using blood microbiome and identification of foods for breast cancer prevention.

The incidence of breast cancer (BC) is increasing in South Korea, and diet is closely related to the high prevalence of BC. The microbiome directly reflects eating habits. In this study, a diagnostic algorithm was developed by analyzing the microbiome patterns of BC. Blood samples were collected from 96 patients with BC and 192 healthy controls. Bacterial extracellular vesicles (EVs) were collected from each blood sample, and next-generation sequencing (NGS) of bacterial EVs was performed. Microbiome analysis of patients with BC and healthy controls identified significantly higher bacterial abundances using EVs in each group and confirmed the receiver operating characteristic (ROC) curves. Using this algorithm, animal experiments were performed to determine which foods affect EV composition. Compared to BC and healthy controls, statistically significant bacterial EVs were selected from both groups, and a receiver operating characteristic (ROC) curve was drawn with a sensitivity of 96.4%, specificity of 100%, and accuracy of 99.6% based on the machine learning method. This algorithm is expected to be applicable to medical practice, such as in health checkup centers. In addition, the results obtained from animal experiments are expected to select and apply foods that have a positive effect on patients with BC.

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects.

Although mounting evidence suggests that the microbiome has a tremendous influence on intractable disease, the relationship between circulating microbial extracellular vesicles (EVs) and respiratory disease remains unexplored. Here, we developed predictive diagnostic models for COPD, asthma, and lung cancer by applying machine learning to microbial EV metagenomes isolated from patient serum and coded by their accumulated taxonomic hierarchy. All models demonstrated high predictive strength with mean AUC values ranging from 0.93 to 0.99 with various important features at the genus and phylum levels. Application of the clinical models in mice showed that various foods reduced high-fat diet-associated asthma and lung cancer risk, while COPD was minimally affected. In conclusion, this study offers a novel methodology for respiratory disease prediction and highlights the utility of serum microbial EVs as data-rich features for noninvasive diagnosis.

A new horizon of precision medicine: combination of the microbiome and extracellular vesicles.

Over several decades, the disease pattern of intractable disease has changed from acute infection to chronic disease accompanied by immune and metabolic dysfunction. In addition, scientific evidence has shown that humans are holobionts; of the DNA in humans, 1% is derived from the human genome, and 99% is derived from microbial genomes (the microbiome). Extracellular vesicles (EVs) are lipid bilayer-delimited nanoparticles and key messengers in cell-to-cell communication. Many publications indicate that microbial EVs are both positively and negatively involved in the pathogenesis of various intractable diseases, including inflammatory diseases, metabolic disorders, and cancers. Microbial EVs in feces, blood, and urine show significant differences in their profiles between patients with a particular disease and healthy subjects, demonstrating the potential of microbial EVs as biomarkers for disease diagnosis, especially for assessing disease risk. Furthermore, microbial EV therapy offers a variety of advantages over live biotherapeutics and human cell EV (or exosome) therapy for the treatment of intractable diseases. In summary, microbial EVs are a new tool in medicine, and microbial EV technology might provide us with innovative diagnostic and therapeutic solutions in precision medicine.

Microbial changes in stool, saliva, serum, and urine before and after anti-TNF-α therapy in patients with inflammatory bowel diseases.

Inflammatory bowel diseases (IBD), including Crohn's disease and ulcerative colitis, are chronic immune-mediated intestinal inflammatory disorders associated with microbial dysbiosis at multiple sites, particularly the gut. Anti-tumor necrosis factor-α (TNF-α) agents are important treatments for IBD. We investigated whether microbiome changes at multiple sites can predict the effectiveness of such treatment in IBD. Stool, saliva, serum, and urine biosamples were collected from 19 IBD patients before (V1) and 3 months after (V2) anti-TNF-α treatment, and 19 healthy subjects (control). Microbiota analysis was performed using extracellular vesicles (EVs; all four sample types) and next-generation sequencing (NGS; stool and saliva). The stool, using NGS analysis, was the only sample type in which α-diversity differed significantly between the IBD and control groups at V1 and V2. Relative to non-responders, responders to anti-TNF-α treatment had significantly higher levels of Firmicutes (phylum), Clostridia (class), and Ruminococcaceae (family) in V1 stool, and Prevotella in V1 saliva. Non-responders had significantly higher V2 serum and urine levels of Lachnospiraceae than responders. Finally, Acidovorax caeni was detected in all V1 sample types in responders, but was not detected in non-responders. Microbiome changes at multiple sites may predict the effectiveness of anti-TNF-α treatment in IBD, warranting further research.

Fecal Microbiota and Gut Microbe-Derived Extracellular Vesicles in Colorectal Cancer.

The human microbiota comprises trillions of microbes, and the relationship between cancer and microbiota is very complex. The impact of fecal microbiota alterations on colorectal cancer (CRC) pathogenesis is emerging. This study analyzed changes in the microbial composition in CRC subjects with both fecal microbiota and gut microbe-derived extracellular vesicles (EVs). From August 2017 to August 2018, 70 CRC patients and 158 control subjects were enrolled in the study. Metagenomic profiling of fecal microbiota and gut microbe-derived EVs in stool was performed using 16S ribosomal DNA sequencing. Relative abundance, evenness, and diversity in both the gut microbiota and gut microbe-derived EVs were analyzed. Additionally, microbial composition changes according to the stage and location of CRC were analyzed. Microbial composition was significantly changed in CRC subjects compared to control subjects, with evenness and diversity significantly lower in the fecal microbiota of CRC subjects. Gut microbe-derived EVs of stool demonstrated significant differences in the microbial composition, evenness, and diversity in CRC subjects compared to the control subjects. Additionally, microbial composition, evenness, and diversity significantly changed in late CRC subjects compared to early CRC subjects with both fecal microbiota and gut microbe-derived EVs. Alistipes-derived EVs could be novel biomarkers for diagnosing CRC and predicting CRC stages. Ruminococcus 2-derived EVs significantly decreased in distal CRC subjects than in proximal CRC subjects. Gut microbe-derived EVs in CRC had a distinct microbial composition compared to the controls. Profiling of microbe-derived EVs may offer a novel biomarker for detecting and predicting CRC prognosis.

The association between serum microbial DNA composition and symptoms of depression and anxiety in mood disorders.

There is increasing evidence supporting the association between gut microbiome composition and mood disorders; however, studies on the circulating microbiome are scarce. This study aimed to analyze the association of the serum microbial DNA composition with depressive and anxiety symptoms in patients with mood disorders. The sera of 69 patients with mood disorders, aged from 19 to 60, were analyzed. Bacterial DNA was isolated from extracellular membrane vesicles and, subsequently, amplified and quantified with specific primers for the V3-V4 hypervariable region of the 16S rDNA gene. Sequence reads were clustered into Operational Taxonomic Units and classified using the SILVA database. There were no significant associations between alpha diversity measures and the total Hamilton depression rating scale (HAM-D) or Beck anxiety inventory (BAI) scores. Only the weighted UniFrac distance was associated with the total HAM-D score (F = 1.57, p = 0.045). The Bacteroidaceae family and Bacteroides genus were negatively associated with the total HAM-D score (ß = - 0.016, p < 0.001, q = 0.08 and ß = - 0.016, p < 0.001, q = 0.15, respectively). The Desulfovibrionaceae family and Clostridiales Family XIII were positively associated with the total BAI score (ß = 1.8 × 10-3, p < 0.001, q = 0.04 and ß = 1.3 × 10-3, p < 0.001, q = 0.24, respectively). Further studies with larger sample sizes and longitudinal designs are warranted.

Bacterial extracellular vesicles affect endocrine therapy in MCF7 cells.

BACKGROUND: : The microbiome is important in the development and progression of breast cancer. This study investigated the effects of microbiome derived from Klebsiella on endocrine therapy of breast cancer using MCF7 cells. The bacterial extracellular vesicles (EVs) that affect endocrine therapy were established through experiments focused on tamoxifen efficacy. METHODS: : The microbiomes of breast cancer patients and healthy controls were analyzed using next-generation sequencing. Among microbiome, Klebsiella was selected as the experimental material for the effect on endocrine therapy in MCF7 cells. MCF7 cells were incubated with tamoxifen in the absence/presence of bacterial EVs derived from Klebsiella pneumoniae and analyzed by quantitative real-time polymerase chain reaction and Western blot. RESULTS: : Microbiome derived from Klebsiella is abundant in breast cancer patients especially luminal A subtype compared to healthy controls. The addition of EVs derived from K pneumoniae enhances the anti-hormonal effects of tamoxifen in MCF7 cells. The increased efficacy of tamoxifen is mediated via Cyclin E2 and p-ERK. CONCLUSION: : Based on experiments, the EVs derived from K pneumoniae are important in hormone therapy on MCF7 cells. This result provides new insight into breast cancer mechanisms and hormone therapy using Klebsiella found in the microbiome.

Microbiome as a potential diagnostic and predictive biomarker in severe alcoholic hepatitis.

BACKGROUND: Severe alcoholic hepatitis (AH) is the most aggressive form of alcohol-related liver disease with high mortality. The microbiome is an emerging therapeutic target in alcohol-related liver disease. AIMS: To investigate the microbiome composition in patients with severe AH, and to determine microbiome recovery after rifaximin treatment in gut bacteria and bacteria derived-extracellular vesicles. METHODS: We enrolled 24 patients with severe AH and 24 healthy controls. Additional faecal samples were collected after 4 weeks in 8 patients with severe AH who completed rifaximin treatment. Treatment response was defined based on Lille score model after 7 days of treatment. Metagenomic profiling was performed using 16S ribosomal RNA amplicon sequencing. RESULTS: Faecal microbiomes of patients with severe AH had lower alpha diversity and higher beta diversity than those of healthy controls in both gut bacteria and extracellular vesicles. Bacilli, Lactobacillales and Veillonella were significantly increased in the gut bacteria of patients with severe AH, and Veillonella, Veillonella parvula group and Lactobacillales were significantly increased in the extracellular vesicles of patients with severe AH. Eubacterium_g23, Oscillibacter and Clostridiales decreased in the gut bacteria of patients with severe AH, and Eubacterium_g23, Oscillibacter and Christensenellaceae decreased in the extracellular vesicles of patients with severe AH. After rifaximin treatment, 17 taxa in the gut bacteria and 23 taxa in extracellular vesicles were significantly restored in patients with severe AH. In common, Veillonella and Veillonella parvula group increased in patients with severe AH and decreased after rifaximin treatment, and Prevotella and Prevotellaceae decreased in patients with severe AH and increased after rifaximin treatment. Treatment non-responders showed a significantly lower abundance of Prevotella at baseline than did treatment responders. CONCLUSION: Dysbiosis was confirmed in severe AH but was alleviated by rifaximin treatment. Taxa associated with severe AH can be candidate biomarkers or therapeutic targets.

Indoor Thermal Environment Long-Term Data Analytics Using IoT Devices in Korean Apartments: A Case Study.

IoT-based monitoring devices can transmit real-time and long-term thermal environment data, enabling innovative conversion for the evaluation and management of the indoor thermal environment. However, long-term indoor thermal measurements using IoT-based devices to investigate health effects have rarely been conducted. Using apartments in Seoul as a case study, we conducted long-term monitoring of thermal environmental using IoT-based real-time wireless sensors. We measured the temperature, relative humidity (RH), and CO2 in the kitchen, living room, and bedrooms of each household over one year. In addition, in one of the houses, velocity and globe temperatures were measured for multiple summer and autumn seasons. Results of our present study indicated that outdoor temperature is an important influencing factor of indoor thermal environment and indoor RH is a good indicator of residents' lifestyle. Our findings highlighted the need for temperature management in summer, RH management in winter, and kitchen thermal environment management during summer and tropical nights. This study suggested that IoT devices are a potential approach for evaluating personal exposure to indoor thermal environmental risks. In addition, long-term monitoring and analysis is an efficient approach for analyzing complex indoor thermal environments and is a viable method for application in healthcare.

Brain tumor diagnostic model and dietary effect based on extracellular vesicle microbiome data in serum.

The human microbiome has been recently associated with human health and disease. Brain tumors (BTs) are a particularly difficult condition to directly link to the microbiome, as microorganisms cannot generally cross the blood-brain barrier (BBB). However, some nanosized extracellular vesicles (EVs) released from microorganisms can cross the BBB and enter the brain. Therefore, we conducted metagenomic analysis of microbial EVs in both serum (152 BT patients and 198 healthy controls (HC)) and brain tissue (5 BT patients and 5 HC) samples based on the V3-V4 regions of 16S rDNA. We then developed diagnostic models through logistic regression and machine learning algorithms using serum EV metagenomic data to assess the ability of various dietary supplements to reduce BT risk in vivo. Models incorporating the stepwise method and the linear discriminant analysis effect size (LEfSe) method yielded 12 and 29 significant genera as potential biomarkers, respectively. Models using the selected biomarkers yielded areas under the curves (AUCs) >0.93, and the model using machine learning resulted in an AUC of 0.99. In addition, Dialister and [Eubacterium] rectale were significantly lower in both blood and tissue samples of BT patients than in those of HCs. In vivo tests showed that BT risk was decreased through the addition of sorghum, brown rice oil, and garlic but conversely increased by the addition of bellflower and pear. In conclusion, serum EV metagenomics shows promise as a rich data source for highly accurate detection of BT risk, and several foods have potential for mitigating BT risk.

Metagenome analysis using serum extracellular vesicles identified distinct microbiota in asthmatics.

Different patterns of bacterial communities have been reported in the airways and gastrointestinal tract of asthmatics when compared to healthy controls. However, the blood microbiome of asthmatics is yet to be investigated. Therefore, we aimed to determine whether a distinct serum microbiome is observed in asthmatics by metagenomic analysis of serum extracellular vesicles (EVs). We obtained serum from 190 adults with asthma and 260 healthy controls, from which EVs were isolated and analyzed. The bacterial composition of asthmatics was significantly different from that of healthy controls. Chao 1 index was significantly higher in the asthma group, while Shannon and Simpson indices were higher in the control group. At the phylum level, Bacteroidetes was more abundant in asthmatics, while Actinobacter, Verrucomicrobia, and Cyanobacteria were more abundant in healthy controls. At the genus level, 24 bacterial genera showed differences in relative abundance between asthmatics and controls, with linear discriminant analysis scores greater than 3. Further, in a diagnostic model based on these differences, a high predictive value with a sensitivity of 0.92 and a specificity of 0.93 was observed. In conclusion, we demonstrated distinct blood microbiome in asthma indicating the role of microbiome as a potential diagnostic marker of asthma.

Clinical Characteristics of Asymptomatic and Symptomatic Pediatric Coronavirus Disease 2019 (COVID-19): A Systematic Review.

Background and objectives: Characterization of pediatric coronavirus disease 2019 (COVID-19) is necessary to control the pandemic, as asymptomatic or mildly infected children may act as carriers. To date, there are limited reports describing differences in clinical, laboratory, and radiological characteristics between asymptomatic and symptomatic infection, and between younger and older pediatric patients. The objective of this study is to compare characteristics among: (1) asymptomatic versus symptomatic and (2) less than 10 versus greater or equal to 10 years old pediatric COVID-19 patients. Materials and Methods: We searched for all terms related to pediatric COVID-19 in electronic databases (Embase, Medline, PubMed, and Web of Science) for articles from January 2020. This protocol followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Results: Eligible study designs included case reports and series, while we excluded comments/letters, reviews, and literature not written in English. Initially, 817 articles were identified. Forty-three articles encompassing 158 confirmed pediatric COVID-19 cases were included in the final analyses. Lymphocytosis and high CRP were associated with symptomatic infection. Abnormal chest CT more accurately detected asymptomatic COVID-19 in older patients than in younger ones, but clinical characteristics were similar between older and younger patients. Conclusions: Chest CT scan findings are untrustworthy in younger children with COVID-19 as compared with clinical findings, or significant differences in findings between asymptomatic to symptomatic children. Further studies evaluating pediatric COVID-19 could contribute to potential therapeutic interventions and preventive strategies to limit spreading.

Association between the microbiomes of tonsil and saliva samples isolated from pediatric patients subjected to tonsillectomy for the treatment of tonsillar hyperplasia.

Oral microbes have the capacity to spread throughout the gastrointestinal system and are strongly associated with multiple diseases. Given that tonsils are located between the oral cavity and the laryngopharynx at the gateway of the alimentary and respiratory tracts, tonsillar tissue may also be affected by microbiota from both the oral cavity (saliva) and the alimentary tract. Here, we analyzed the distribution and association of the microbial communities in the saliva and tonsils of Korean children subjected to tonsillectomy because of tonsil hyperplasia (n = 29). The microbiome profiles of saliva and tonsils were established via 16S rRNA gene sequencing. Based on the alpha diversity indices, the microbial communities of the two groups showed high similarities. According to Spearman's ranking correlation analysis, the distribution of Treponema, the causative bacterium of periodontitis, in saliva and tonsils was found to have a significant positive correlation. Two representative microbes, Prevotella in saliva and Alloprevotella in tonsils, were negatively correlated, while Treponema 2 showed a strong positive correlation between saliva and tonsils. Taken together, strong similarities in the microbial communities of the tonsils and saliva are evident in terms of diversity and composition. The saliva microbiome is expected to significantly affect the tonsil microbiome. Furthermore, we suggest that our study creates an opportunity for tonsillar microbiome research to facilitate the development of novel microbiome-based therapeutic strategies.

Diagnostic Models for Atopic Dermatitis Based on Serum Microbial Extracellular Vesicle Metagenomic Analysis: A Pilot Study.

PURPOSE: Associations between a wide variety of diseases and the microbiome have been extensively verified. Recently, there has been a rising interest in the role the microbiome plays in atopic dermatitis (AD). Furthermore, metagenomic analysis of microbe-derived extracellular vesicles (EVs) has revealed the importance and relevance of microbial EVs in human health. METHODS: We compared the diversity and proportion of microbial EVs in the sera of 24 AD patients and 49 healthy controls, and developed a diagnostic model. After separating microbial EVs from serum, we specifically targeted the V3-V4 hypervariable regions of the 16S rDNA gene for amplification and subsequent sequencing. RESULTS: Alpha and beta diversity between controls and AD patients both differed, but only the difference in beta diversity was significant. Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla in healthy controls and AD patients, accounting for over 85% of the total serum bacterial EVs. Also, Proteobacteria, Firmicutes, Actinobacteria, Verrucomicrobia, and Cyanobacteria relative abundances were significantly different between the AD and control groups. At the genus level, the proportions of Escherichia-Shigella, Acinetobacter, Pseudomonas, and Enterococcus were drastically altered between the AD and control groups. AD diagnostic models developed using biomarkers selected on the basis of linear discriminant analysis effect size from the class to genus levels all yielded area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of value 1.00. CONCLUSIONS: In summary, microbial EVs demonstrated the potential in their use as novel biomarkers for AD diagnosis. Therefore, future work should investigate larger case and control groups with cross-sectional or longitudinal clinical data to explore the utility and validity of serum microbiota EV-based AD diagnosis.

Lung Disease Diagnostic Model Through IgG Sensitization to Microbial Extracellular Vesicles.

PURPOSE: Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease. METHODS: We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis. RESULTS: Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81. CONCLUSIONS: The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.

Metagenomic Analysis of Serum Microbe-Derived Extracellular Vesicles and Diagnostic Models to Differentiate Ovarian Cancer and Benign Ovarian Tumor.

We aimed to develop a diagnostic model identifying ovarian cancer (OC) from benign ovarian tumors using metagenomic data from serum microbe-derived extracellular vesicles (EVs). We obtained serum samples from 166 patients with pathologically confirmed OC and 76 patients with benign ovarian tumors. For model construction and validation, samples were randomly divided into training and test sets in the ratio 2:1. Isolation of microbial EVs from serum samples of the patients and 16S rDNA amplicon sequencing were carried out. Metagenomic and clinicopathologic data-based OC diagnostic models were constructed in the training set and then validated in the test set. There were significant differences in the metagenomic profiles between the OC and benign ovarian tumor groups; specifically, genus Acinetobacter was significantly more abundant in the OC group. More importantly, Acinetobacter was the only common genus identified by seven different statistical analysis methods. Among the various metagenomic and clinicopathologic data-based OC diagnostic models, the model consisting of age, serum CA-125 levels, and relative abundance of Acinetobacter showed the best diagnostic performance with the area under the receiver operating characteristic curve of 0.898 and 0.846 in the training and test sets, respectively. Thus, our findings establish a metagenomic analysis of serum microbe-derived EVs as a potential tool for the diagnosis of OC.

Thermal Conductivity Characterization of Thermal Grease Containing Copper Nanopowder.

As electronic devices and mainboards become smaller, the need for thermal conductive materials having excellent internal heat dissipation is increasing. In this study, nano thermal grease was prepared by mixing in copper nanopowder, which is used as a heat transfer medium in thermal grease, which is a kind of thermal conductive material, with silicon oil. In addition, copper powder was mixed with graphene and alumina, respectively, and the thermal conductivity performance was compared. As a result, the thermal conductivity improved by 4.5 W/m·k over the silicon base, and the upward trend of thermal conductivity increased steadily up to 15 vol. %, and the increasing trend decreased after 20 vol. %. In addition, the increased rate of thermal conductivity from 0 to 5 vol. % and 10 to 15 vol. % was the largest.

The impact of bacteria-derived ultrafine dust particles on pulmonary diseases.

The relationship between ambient particulate matter exposure and health has been well established. Ultrafine particles (UFP) with a diameter of 100 nm or less are known to increase pulmonary disease risk. Biological factors in dust containing UFP can cause severe inflammatory reactions. Pulmonary diseases develop primarily as a result of chronic inflammation caused by immune dysfunction. Thus, this review focuses on the adverse pulmonary effects of biological UFP, principally lipopolysaccharide (LPS), and bacterial extracellular vesicles (EVs), in indoor dust and the pathophysiological mechanisms involved in the development of chronic pulmonary diseases. The impact of LPS-induced pulmonary inflammation is based primarily on the amount of inhaled LPS. When relatively low levels of LPS are inhaled, a cascade of immune responses leads to Th2 cell induction, and IL-5 and IL-13 released by Th2 cells contributes to asthma development. Conversely, exposure to high levels of LPS induces a Th17 cell response, leading to increased production of IL-17, which is associated with asthma, COPD, and lung cancer incidence. Responses to bacterial EV exposure can similarly be broadly divided based on whether one of two mechanisms, either intracellular or extracellular, is activated, which depends on the type of the parent cell. Extracellular bacteria-derived EVs can cause neutrophilic inflammation via Th17 cell induction, which is associated with asthma, emphysema, COPD, and lung cancer. On the other hand, intracellular bacteria-derived EVs lead to mononuclear inflammation via Th1 cell induction, which increases the risk of emphysema. In conclusion, future measures should focus on the overall reduction of LPS sources in addition to the improvement of the balance of inhaled bacterial EVs in the indoor environment to minimize pulmonary disease risk.

Colorectal cancer diagnostic model utilizing metagenomic and metabolomic data of stool microbial extracellular vesicles.

Colorectal cancer (CRC) is the most common type cancers in the world. CRC occurs sporadically in the majority of cases, indicating the predominant cause of the disease are environmental factors. Diet-induced changes in gut-microbiome are recently supposed to contribute on epidemics of CRC. This study was aimed to investigate the association of metagenomics and metabolomics in gut extracellular vesicles (EVs) of CRC and healthy subjects. A total of 40 healthy volunteers and 32 patients with CRC were enrolled in this study. Metagenomic profiling by sequencing 16 S rDNA was performed for assessing microbial codiversity. We explored the small molecule metabolites using gas chromatography-time-of-flight mass spectrometry. In total, stool EVs were prepared from 40 healthy volunteers and 32 patients with CRC. Metagenomic profiling demonstrated that bacterial phyla, particularly of Firmicutes and Proteobacteria, were significantly altered in patients with colorectal cancer. Through metabolomics profiling, we determined seven amino acids, four carboxylic acids, and four fatty acids; including short-chain to long chain fatty acids that altered in the disease group. Binary logistic regression was further tested to evaluate the diagnostic performance. In summary, the present findings suggest that gut flora dysbiosis may result in alternation of amino acid metabolism, which may be correlated with the pathogenesis of CRC.