Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: µECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

Melatonin partially rescues defects induced by tranexamic acid exposure during oocyte maturation in mice.

Tranexamic acid (TXA) is widely used among young women because of its ability to whiten skin and treat menorrhagia. Nevertheless, its potential effects on oocyte maturation and quality have not yet been clearly clarified. Melatonin (MT) is an endogenous hormone released by the pineal gland and believed to protect cells from oxidative stress injury. In the present study, we used an in vitro maturation model to investigate the toxicity of TXA and the protective role of MT in mouse oocytes. Compared with the control group, the TXA-exposed group had significantly lower nuclear maturation (57.72% vs. 94.08%, P < 0.001) and early embryo cleavage rates (38.18% vs. 87.66%, P < 0.001). Further study showed that spindle organization (52.56% vs. 18.77%, P < 0.01) and chromosome alignment (33.23% vs. 16.66%, P < 0.01) were also disrupted after TXA treatment. Mechanistically, we have demonstrated that TXA induced early apoptosis of oocytes (P < 0.001) by raising the level of reactive oxygen species (P < 0.001), which was consistent with an increase in mitochondrial damage (P < 0.01). Fortunately, all these effects except the spindle defect were successfully rescued by an appropriate level of MT. Collectively, our findings indicate that MT could partially reverse TXA-induced oocyte quality deterioration in mice by effectively improving mitochondrial function and reducing oxidative stress-mediated apoptosis.NEW & NOTEWORTHY Tranexamic acid is increasingly used to whiten skin, reverse dermal damages, and treat heavy menstrual bleeding in young women. However, its potential toxicity in mammalian oocytes is still unclear. Our study revealed that tranexamic acid exposure impaired the mouse oocyte quality and subsequent embryo development. Meanwhile, melatonin has been found to exert beneficial effects in reducing tranexamic acid-induced mitochondrial dysfunction and oxidative stress.

Clinical best practices in interdisciplinary management of human epidermal growth factor receptor 2 antibody-drug conjugates-induced interstitial lung disease/pneumonitis: An expert consensus in China.

Antibody-drug conjugates (ADCs) have demonstrated effectiveness in treating various cancers, particularly exhibiting specificity in targeting human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Recent advancements in phase 3 clinical trials have broadened current understanding of ADCs, especially trastuzumab deruxtecan, in treating other HER2-expressing malignancies. This expansion of knowledge has led to the US Food and Drug Administration's approval of trastuzumab deruxtecan for HER2-positive and HER2-low breast cancer, HER2-positive gastric cancer, and HER2-mutant nonsmall cell lung cancer. Concurrent with the increasing use of ADCs in oncology, there is growing concern among health care professionals regarding the rise in the incidence of interstitial lung disease or pneumonitis (ILD/p), which is associated with anti-HER2 ADC therapy. Studies on anti-HER2 ADCs have reported varying ILD/p mortality rates. Consequently, it is crucial to establish guidelines for the diagnosis and management of ILD/p in patients receiving anti-HER2 ADC therapy. To this end, a panel of Chinese experts was convened to formulate a strategic approach for the identification and management of ILD/p in patients treated with anti-HER2 ADC therapy. This report presents the expert panel's opinions and recommendations, which are intended to guide the management of ILD/p induced by anti-HER2 ADC therapy in clinical practice.

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.

BACKGROUND: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. METHODS: Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. RESULTS: In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). CONCLUSION: Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. CLINICAL TRIAL REGISTRATION: This study is not a clinical trial and was therefore not registered.

USP9X-mediated REV1 deubiquitination promotes lung cancer radioresistance via the action of REV1 as a Rad18 molecular scaffold for cystathionine γ-lyase.

BACKGROUND: Radioresistance is a key clinical constraint on the efficacy of radiotherapy in lung cancer patients. REV1 DNA directed polymerase (REV1) plays an important role in repairing DNA damage and maintaining genomic stability. However, its role in the resistance to radiotherapy in lung cancer is not clear. This study aims to clarify the role of REV1 in lung cancer radioresistance, identify the intrinsic mechanisms involved, and provide a theoretical basis for the clinical translation of this new target for lung cancer treatment. METHODS: The effect of targeting REV1 on the radiosensitivity was verified by in vivo and in vitro experiments. RNA sequencing (RNA-seq) combined with nontargeted metabolomics analysis was used to explore the downstream targets of REV1. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to quantify the content of specific amino acids. The coimmunoprecipitation (co-IP) and GST pull-down assays were used to validate the interaction between proteins. A ubiquitination library screening system was constructed to investigate the regulatory proteins upstream of REV1. RESULTS: Targeting REV1 could enhance the radiosensitivity in vivo, while this effect was not obvious in vitro. RNA sequencing combined with nontargeted metabolomics revealed that the difference result was related to metabolism, and that the expression of glycine, serine, and threonine (Gly/Ser/Thr) metabolism signaling pathways was downregulated following REV1 knockdown. LC-MS/MS demonstrated that REV1 knockdown results in reduced levels of these three amino acids and that cystathionine γ-lyase (CTH) was the key to its function. REV1 enhances the interaction of CTH with the E3 ubiquitin ligase Rad18 and promotes ubiquitination degradation of CTH by Rad18. Screening of the ubiquitination compound library revealed that the ubiquitin-specific peptidase 9 X-linked (USP9X) is the upstream regulatory protein of REV1 by the ubiquitin-proteasome system, which remodels the intracellular Gly/Ser/Thr metabolism. CONCLUSION: USP9X mediates the deubiquitination of REV1, and aberrantly expressed REV1 acts as a scaffolding protein to assist Rad18 in interacting with CTH, promoting the ubiquitination and degradation of CTH and inducing remodeling of the Gly/Ser/Thr metabolism, which leads to radioresistance. A novel inhibitor of REV1, JH-RE-06, was shown to enhance lung cancer cell radiosensitivity, with good prospects for clinical translation.

A new artificial intelligence system for both stomach and small-bowel capsule endoscopy.

BACKGROUND AND AIMS: Despite the benefits of artificial intelligence in small-bowel (SB) capsule endoscopy (CE) image reading, information on its application in the stomach and SB CE is lacking. METHODS: In this multicenter, retrospective diagnostic study, gastric imaging data were added to the deep learning-based SmartScan (SS), which has been described previously. A total of 1069 magnetically controlled GI CE examinations (comprising 2,672,542 gastric images) were used in the training phase for recognizing gastric pathologies, producing a new artificial intelligence algorithm named SS Plus. A total of 342 fully automated, magnetically controlled CE examinations were included in the validation phase. The performance of both senior and junior endoscopists with both the SS Plus-assisted reading (SSP-AR) and conventional reading (CR) modes was assessed. RESULTS: SS Plus was designed to recognize 5 types of gastric lesions and 17 types of SB lesions. SS Plus reduced the number of CE images required for review to 873.90 (median, 1000; interquartile range [IQR], 814.50-1000) versus 44,322.73 (median, 42,393; IQR, 31,722.75-54,971.25) for CR. Furthermore, with SSP-AR, endoscopists took 9.54 minutes (median, 8.51; IQR, 6.05-13.13) to complete the CE video reading. In the 342 CE videos, SS Plus identified 411 gastric and 422 SB lesions, whereas 400 gastric and 368 intestinal lesions were detected with CR. Moreover, junior endoscopists remarkably improved their CE image reading ability with SSP-AR. CONCLUSIONS: Our study shows that the newly upgraded deep learning-based algorithm SS Plus can detect GI lesions and help improve the diagnostic performance of junior endoscopists in interpreting CE videos.

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells.

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t - 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.

Strain-induced ferroelectric polarization reversal without undergoing geometric inversion in blue SiSe monolayer.

Ferroelectricity in two-dimensional (2D) systems generally arises from phonons and has been widely investigated. On the contrary, electronic ferroelectricity in 2D systems has been rarely studied. Using first-principles calculations, the ferroelectric behavior of the buckled blue SiSe monolayer under strain are explored. It is found that the direction of the out-of-plane ferroelectric polarization can be reversed by applying an in-plane strain. And such polarization switching is realized without undergoing geometric inversion. Besides, the strain-triggered polarization reversal emerges in both biaxial and uniaxial strain cases, indicating it is an intrinsic feature of such a system. Further analysis shows that the polarization switching is the result of the reversal of the magnitudes of the positive and negative charge center vectors. And the variation of buckling is found to play an important role, which results in the switch. Moreover, a non-monotonic variation of band gap with strain is revealed. Our findings throws light on the investigation of novel electronic ferroelectric systems.

Ecological investigations of giant Phaeocystis colonies in Viet Nam: II. Photosynthesis-irradiance characteristics and nitrogen uptake.

Phaeocystis globosa is a marine phytoplankton species that forms deleterious blooms in temperate and tropical waters. In some locations, "giant" colonies form, although the controls on its size are unknown. During a "giant" colony bloom, measurements were completed to characterize photosynthesis-irradiance relationships, nitrogen uptake kinetics, and nitrogen-irradiance relationships of P. globosa colonies to understand its growth characteristics and their relationship to colony size. The photosynthetic capacity (Fv/Fm) varied from 0.65 to 0.68 among colony sizes ranging from 3.0 to 11.0 mm, indicating that all colonial cells were physiologically robust. The maximum chl a-specific photosynthetic rates ( P max B ) ranged from 0.89 to 1.92 µg C · µg-1 chl · h-1, were maximal in the mid-sized colonies (5.5-6.5 mm) and decreased with size. The relatively low P max B values may be related to the high cellular chl a of colonial cells and their acclimation to in situ irradiance. Nitrate V max and K S values were greater than those of ammonium, although N affinity was greater for ammonium. No differences in light-limited rates in either nitrate or ammonium uptake among colony sizes were observed, and no dark uptake occurred. Both ammonium and nitrate uptake showed a saturation response as a function of irradiance. While the driving forces for the formation of giant colonies remain unknown, their impacts on coastal systems are substantial and a further assessment of their growth is warranted.

Ecological investigations of giant Phaeocystis colonies in Viet Nam: I. Cell abundance and elemental composition.

Phaeocystis globosa is an important bloom-forming marine phytoplankton species that often accumulates to large levels in temperate and tropical waters and has significant impacts on food webs and biogeochemical cycles. It can form "giant" colonies that reach 3 cm in diameter. Microscopic observations, colony elemental composition, and pigment composition were analyzed to assess the characteristics of colonies as a function of colony size. Particulate organic carbon (POC) per unit surface area, colonial cell density, and chlorophyll a per unit surface area all increased with colony size, in contrast to results from temperate waters. Cellular chl a averaged 0.85 pg chl · cell-1. Colonies had both photosynthetic and protective pigments, with fucoxanthin being the dominant accessory pigment. Based on chl a and pigment levels, it appears colonies were acclimated to relatively low irradiances, likely due to their life cycle and the extremely turbulent environment in which they grew. Mucous carbon ranged from 16.2% to 79.2% of the total POC, and mucous carbon per unit surface area increased with colony size, suggesting that the mucous envelope did not thin as the colony grew. Based on elemental composition, nitrogen did not appear to limit growth, but phosphorus:carbon ratios were similar to those of P-limited cultures. Giant colonies represent an extreme response to the environment, but they do not appear to have greatly different characteristics than other tropical strains.

Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset.

BACKGROUND: Obesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health. METHODS: This research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models-XG Boost, Support Vector Machine (SVM), and Random Forest (RF)-were employed. RESULTS: Univariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance. CONCLUSIONS: The research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.

A bird's-eye view of research practices in mathematical cognition, learning, and instruction: Reimagining the status quo.

Research on mathematical cognition, learning, and instruction (MCLI) often takes cognition as its point of departure and considers instruction at a later point in the research cycle. In this article, we call for psychologists who study MCLI to reflect on the "status quo" of their research practices and to consider making instruction an earlier and more central aspect of their work. We encourage scholars of MCLI (a) to consider the needs of educators and schools when selecting research questions and developing interventions; (b) to compose research teams that are diverse in the personal, disciplinary, and occupational backgrounds of team members; (c) to make efforts to broaden participation in research and to conduct research in authentic settings; and (d) to communicate research in ways that are accessible to practitioners and to the general public. We argue that a more central consideration of instruction will lead to shifts that make research on MCLI more theoretically valuable, more actionable for educators, and more relevant to pressing societal challenges.

Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey.

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction.

The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity testing of new compounds is very necessary before being put on the market. Currently, many methods have been used to predict the carcinogenicity of compounds. However, most methods have limited predictive power and there is still much room for improvement. In this study, we construct a deep learning model based on capsule network and attention mechanism named DCAMCP to discriminate between carcinogenic and non-carcinogenic compounds. We train the DCAMCP on a dataset containing 1564 different compounds through their molecular fingerprints and molecular graph features. The trained model is validated by fivefold cross-validation and external validation. DCAMCP achieves an average accuracy (ACC) of 0.718 ± 0.009, sensitivity (SE) of 0.721 ± 0.006, specificity (SP) of 0.715 ± 0.014 and area under the receiver-operating characteristic curve (AUC) of 0.793 ± 0.012. Meanwhile, comparable results can be achieved on an external validation dataset containing 100 compounds, with an ACC of 0.750, SE of 0.778, SP of 0.727 and AUC of 0.811, which demonstrate the reliability of DCAMCP. The results indicate that our model has made progress in cancer risk assessment and could be used as an efficient tool in drug design.

Role of EBAF/Nodal/p27 signaling pathway in development of placenta in normal and diabetic rats.

Placentas control the maternal-fetal transport of nutrients and gases. Placental reactions to adverse intrauterine conditions affect fetal development. Such adverse conditions occur in pregnancies complicated by diabetes, leading to alterations in placental anatomy and physiology. In this study, streptozocin (STZ) injection produced sustained hyperglycemia during pregnancy in rats. Hyperglycemic pregnant rats had gained significantly less weight than normal pregnant rats on embryonic day 15.5. We investigated the influence of diabetes on placental anatomy and physiology. Compared with controls, the diabetic group had a markedly thicker junctional zone at embryonic day 15.5. To explore a mechanism for this abnormality, we examined Nodal expression in the junctional zone of control and diabetic groups. We found lower expression of Nodal in the diabetic group. We then investigated the expression of its target gene p27Kip1 (p27), which is related to cell proliferation. In vitro, Nodal overexpression up-regulated p27 protein levels while interfered EBAF up-regulated p27. In vivo, the expression of p27 was lower in diabetic compared with normal rats, and localization was similar between the two groups. In contrast, a higher expression of PCNA was found in diabetic versus normal placenta. Endometrial bleeding associated factor (EBAF), an up-stream molecular regulator of Nodal, was expressed at higher levels in placenta from diabetic versus normal rats. Based on these results, we speculate that the EBAF/Nodal/p27 signaling pathway plays a role in morphological change of diabetic placenta.

Targeting polymerase θ impairs tumorigenesis and enhances radiosensitivity in lung adenocarcinoma.

Radioresistance remains a major obstacle to efficacious radiotherapy in non-small-cell lung cancer (NSCLC). DNA replication proteins are novel targets for radiosensitizers. POLQ is a DNA polymerase involved in DNA damage response and repair. We found that POLQ is overexpressed in NSCLC and is clinically correlated with high tumor stage, poor prognosis, increased tumor mutational burden, and ALK and TP5 mutation status; POLQ inhibition impaired lung tumorigenesis. Notably, POLQ expression was higher in radioresistant lung cancer cells than in wild-type cancer cells. Moreover, POLQ expression was further increased in radioresistant cells after radiation. Enhanced radioresistance is through a prolonged G2/M phase and faster repair of DNA damage, leading to reduced radiation-induced apoptosis. Novobiocin (NVB), a POLQ inhibitor, specifically targeted cancer cells. Genetic knockdown of POLQ or pharmacological inhibition by NVB decreased radioresistance in lung adenocarcinoma while causing little toxicity to normal pulmonary epithelial cells. In conclusion, POLQ is a promising and practical cancer-specific target to impair tumorigenesis and enhance radiosensitivity in NSCLC.

A meta-analysis of associations of IL-10 gene polymorphisms with acute leukemia susceptibility.

BACKGROUND: Recently, increasing evidence has demonstrated that IL-10 single nucleotide polymorphisms (SNPs) are associated with the risk of acute leukemia (AL), but the findings of different articles remain controversial. Thus, we performed a meta-analysis to further investigate the exact roles of IL-10 SNPs in AL susceptibility. METHODS: Six common Chinese and English databases were utilized to retrieve eligible studies. The strength of the association was assessed by calculating odds ratios and 95 % confidence intervals. All analyses were carried out using Review Manager (version 5.3) and STATA (version 15.1). The registered number of this research is CRD42022373362. RESULTS: A total of 6391 participants were enrolled in this research. The results showed that the AG genotype of rs1800896 increased AL risk in the heterozygous codominant model (AG vs. AA, OR = 1.41, 95 % CI = 1.04-1.92, P = 0.03) and overdominant model (AG vs. AA + GG, OR = 1.32, 95 % CI = 1.04-1.70, P = 0.03). In the subgroup analysis, associations between the G allele, GG genotype, AG genotype, AG + GG genotype of rs1800896 and increased AL risk were also observed in the mixed population based on allelic, homozygote codominant, heterozygous codominant, dominant, and overdominant models. Furthermore, an association between the AC genotype of rs1800872 and increased AL risk was observed in the Caucasian population in the overdominant model. However, the rs1800871, rs3024489 and rs3024493 polymorphisms did not affect AL risk. CONCLUSION: IL-10 rs1800896 and rs1800872 affected the susceptibility of AL and therefore may be biomarkers for early screening and risk prediction of AL.

Activation of NLRP3 inflammasome in lung epithelial cells triggers radiation-induced lung injury.

BACKGROUND: Radiation-induced lung injury (RILI) is the most common and serious complication of chest radiotherapy. However, reported radioprotective agents usually lead to radiation resistance in tumor cells. The key to solving this problem is to distinguish between the response of tumor cells and normal lung epithelial cells to radiation damage. METHODS: RNA-Seq was used to recognize potential target of alleviating the progression of RILI as well as inhibiting tumor growth. The activation of NLRP3 inflammasome in lung epithelial cells was screened by qRT-PCR, western blotting, immunofluorescence, and ELISA. An in vivo model of RILI and in vitro conditioned culture model were constructed to evaluate the effect of NLRP3/interleukin-1ß on fibroblasts activation. ROS, ATP, and (NADP)+/NADP(H) level in lung epithelial cells was detected to explore the mechanism of NLRP3 inflammasome activation. The lung macrophages of the mice were deleted to evaluate the role of lung epithelial cells in RILI. Moreover, primary cells were extracted to validate the results obtained from cell lines. RESULTS: NLRP3 activation in epithelial cells after radiation depends on glycolysis-related reactive oxygen species accumulation. DPYSL4 is activated and acts as a negative regulator of this process. The NLRP3 inflammasome triggers interleukin-1ß secretion, which directly affects fibroblast activation, proliferation, and migration, eventually leading to lung fibrosis. CONCLUSIONS: Our study suggests that NLRP3 inflammasome activation in lung epithelial cells is essential for radiation-induced lung injury. These data strongly indicate that targeting NLRP3 may be effective in reducing radiation-induced lung injury in clinical settings.

Gut microbiota composition can reflect immune responses of latent tuberculosis infection in patients with poorly controlled diabetes.

BACKGROUND: Diabetes mellitus (DM) is a major risk factor for tuberculosis (TB). Evidence has linked the DM-related dysbiosis of gut microbiota to modifiable host immunity to Mycobacterium tuberculosis infection. However, the crosslinks between gut microbiota composition and immunological effects on the development of latent TB infection (LTBI) in DM patients remain uncertain. METHODS: We prospectively obtained stool, blood samples, and medical records from 130 patients with poorly-controlled DM (pDM), defined as ever having an HbA1c > 9.0% within previous 1 year. Among them, 43 had LTBI, as determined by QuantiFERON-TB Gold in-Tube assay. The differences in the taxonomic diversity of gut microbiota between LTBI and non-LTBI groups were investigated using 16S ribosomal RNA sequencing, and a predictive algorithm was established using a random forest model. Serum cytokine levels were measured to determine their correlations with gut microbiota. RESULTS: Compared with non-LTBI group, the microbiota in LTBI group displayed a similar alpha-diversity but different beta-diversity, featuring decrease of Prevotella_9, Streptococcus, and Actinomyces and increase of Bacteroides, Alistipes, and Blautia at the genus level. The accuracy was 0.872 for the LTBI prediction model using the aforementioned 6 microbiome-based biomarkers. Compared with the non-LTBI group, the LTBI group had a significantly lower serum levels of IL-17F (p = 0.025) and TNF-α (p = 0.038), which were correlated with the abundance of the aforementioned 6 taxa. CONCLUSIONS: The study results suggest that gut microbiome composition maybe associated with host immunity relevant to TB status, and gut microbial signature might be helpful for the diagnosis of LTBI.

Multivalent Interactions Enable the Natural Alkaline Amino Acids to Transmute into Macroscopic Adhesive Materials.

Natural alkaline amino acids (aAAs) have been found to interact with tannic acid (TA) in aqueous solution via multiple noncovalent interactions, giving rise to the formation of water-immiscible supramolecular copolymers (aAAs/TA). The driving forces and the internal structures of the supramolecular copolymers were characterized by nuclear magnetic resonance (NMR), X-ray photoelectron spectroscopy (XPS), ζ-potential, elemental analysis (EA), and scanning electron microscopy (SEM). Rheological and lap shear adhesion measurements identify that the aAAs/TA soft materials exhibit wet and underwater adhesion, shear thinning, and self-healing behavior. This supramolecular adhesive can be utilized as both injectable materials and self-gelling powder. Another feature of the aAAs/TA adhesives is the acceptable cellular compatibility with L-929 cells, which enables the supramolecular copolymers to be potential soft materials for health care and bio-related applications. The work highlights that the cross-linked supramolecular polymerization strategy enables minimalistic biomolecules to emulate the functions of complicated proteins secreted by aquatic organisms.