SERS-based AI diagnosis of lung and gastric cancer via exhaled breath.

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

Wearable intelligent sweat platform for SERS-AI diagnosis of gout.

For the past few years, sweat analysis for health monitoring has attracted increasing attention benefiting from wearable technology. In related research, the sensitive detection of uric acid (UA) in sweat with complex composition based on surface-enhanced Raman spectroscopy (SERS) for the diagnosis of gout is still a significant challenge. Herein, we report a visualized and intelligent wearable sweat platform for SERS detection of UA in sweat. In this wearable platform, the spiral channel consisted of colorimetric paper with Ag nanowires (AgNWs) that could capture sweat for SERS measurement. With the help of photos from a smartphone, the pH value and volume of sweat could be quantified intelligently based on the image recognition technique. To diagnose gout, SERS spectra of human sweat with UA are collected in this wearable intelligent platform and analyzed by artificial intelligence (AI) algorithms. The results indicate that the artificial neural network (ANN) algorithm exhibits good identification of gout with high accuracy at 97%. Our work demonstrates that SERS-AI in a wearable intelligent sweat platform could be a feasible strategy for diagnosis of gout, which expands research on sweat analysis for comfortable and noninvasive health monitoring.

Variation in Bombyx mori immune response against fungal pathogen Beauveria bassiana with variability in cell wall ß-1,3-glucan.

Entomopathogenic fungi are protected by a cell wall with dynamic structure for adapting to various environmental conditions. ß-1,3-Glucan recognition proteins activate the innate immune system of insects by recognizing surface molecules of fungi. However, the associations between pathogenicity and the different components of entomopathogenic fungal cell walls remain unclear. Three Beauveria bassiana strains were selected that have significantly differing virulence against Bombyx mori. The molecular mechanisms underlying the immune response in B. mori were investigated using RNA sequencing, which revealed differences in the immune response to different B. bassiana strains at 12 h post-infection. Immunofluorescence assays revealed that ß-1,3-glucan content had an opposite trend to that of fungal virulence. ß-1,3-Glucan injection upregulated BmßGRP4 expression and significantly reduced the virulence of the high-virulence strain but not that of the medium-virulence or low-virulence strains. BmßGRP4 silencing in B. mori with RNA interference resulted in the opposite virulence pattern, indicating that the virulence of B. bassiana was affected by the cell walls' content of ß-1,3-glucan, which could be recognized by BmßGRP4. Furthermore, interference with the gene CnA (calcineurin catalytic A subunit) involved in ß-1,3-glucan synthesis eliminated differences in virulence between B. bassiana strains. These results indicate that strains of a single species of pathogenic fungi that have differing cell wall components are recognized differently by the innate immune system of B. mori.

Combined early palliative care for non-small-cell lung cancer patients: a randomized controlled trial in Chongqing, China.

Purpose: More effective approaches are needed to improve the prognosis of non-small-cell lung cancer (NSCLC) patients. Thus, we used the E-warm model to assess how early integration of interdisciplinary palliative care was related to the quality of life (QoL), psychological functioning, pain management, and nutrition factors of NSCLC patients. Methods: This randomized controlled trial enrolled 280 newly diagnosed NSCLC patients, which were randomly divided (1:1) into combined early palliative care (CEPC) and standard oncological care (SC) groups. At baseline and after 24 weeks, the Functional Assessment of Cancer Therapy-Lung (FACT-L) scale, Hospital Anxiety and Depression Scale (HADS), and the Patient Health Questionnaire-9 (PHQ-9) were used to assess QoL and psychological function, respectively. The Numerical Rating Scale (NRS) and Patient-Generated Subjective Global Assessment (PG-SGA) were used to assess cancer patients' pain and nutrition levels. The primary outcome was overall survival (OS). Secondary outcomes comprised changes in the QoL, psychological functioning, pain, and nutrition state. The intention-to-treat method was applied for analysis. This study was registered at www.chictr.org.cn (ChiCTR2200062617). Results: Of the 140 patients enrolled in the CEPC and SC groups, 102 and 82 completed the research. The CEPC group presented higher QoL than the SC group (p < 0.05). Additionally, fewer patients presented depressive symptoms in the CEPC group than in the SC group (p < 0.05), as well as better nutritional status (p = 0.007) and pain management (p = 0.003). Compared to the SC group, CEPC patients had significantly longer OS (20.4 vs. 24.6 months, p = 0.042; HR: 0.19; 95% CI: 0.04-0.85, p = 0.029). Conclusion: With combined early palliative care, NSCLC patients lived longer, had better QoL, were psychologically stable, were in less pain, and were more nutritionally satisfied.

Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core-shell nanochains.

Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical molecular fingerprint information, and immunity to humidity. Here, an accurate diagnosis of oral cancer (OC) is demonstrated using artificial intelligence (AI)-based SERS of exhaled breath in plasmonic-metal organic framework (MOF) nanoparticles. These plasmonic-MOF nanoparticles were prepared using a zeolitic imidazolate framework coated on Ag nanowires (Ag NWs@ZIF), which offers Raman enhancement from the plasmonic nanowires and gas enrichment from the ZIF shells. Then, the core-shell nanochains of Ag NWs@ZIF prepared with 0.5 mL Ag NWs were selected to capture gaseous methanethiol, which is a tumor biomarker, from the exhalation of OC patients. The substrate was used to collect a total of 400 SERS spectra of exhaled breath of simulated healthy people and simulated OC patients. The artificial neural network (ANN) model in the AI algorithm was trained with these SERS spectra and could classify them with an accuracy of 99%. Notably, the model predicted OC with an area under the curve (AUC) of 0.996 for the simulated OC breath samples. This work suggests the great potential of the combination of breath analysis and AI as a method for the early-stage diagnosis of oral cancer.

Preparation of magnetic Janus microparticles for the rapid removal of microplastics from water.

The continuous spread of microplastics in aquatic environments poses a growing concern and a potential risk to human health. To address this concern, this paper presents a novel approach using magnetic Janus microparticles (MJMs) synthesized via a modified Pickering emulsion method with aminated Fe3O4@SiO2 as the raw material. The effectiveness of these MJMs in removing polystyrene (PS) and polyethylene (PE) microplastics from water was investigated. Paraffin was employed as the masking agent, while N-Octadecylphosphosphonic acid (PAC18) was used as the graft material for MJM preparation. The resulting particles exhibited a distinctive asymmetric flower-shaped structure on the surface, which was confirmed through various analytical techniques including FTIR, TGA, SEM, and water phase contact angle analysis. The MJMs demonstrated exceptional efficiency in adsorbing microplastics. With a microplastic suspension concentration of 2 mg/mL and an adsorbent dosage of 1 mg/mL, the MJMs can attain removal efficiencies of 92.08 % for PS and 60.67 % for PE in just 20 min of contact time. The effectiveness of the adsorption process was attributed to several factors, including hydrophobic interactions, cation-π interactions, electrostatic attraction, and the efficient dispersion of particles in water, as revealed by size distribution and zeta potential analysis. Additionally, kinetic and thermodynamic studies confirmed the remarkable adsorption rate and capacity of the MJMs (0.759 min-1 and 2.72 mg/mg for PS, 0.539 min-1 and 2.42 mg/mg for PE), highlighting their potential as a promising method for rapidly removing microplastics from water. This work provides valuable insights into the development of effective strategies for addressing microplastic pollution in aquatic environments.

Values mediated emotional adjustment by emotion regulation: A longitudinal study among adolescents in China.

Values have a direct impact on adolescents' mental health. However, its potential mediated mechanism has received little attention. A 1-year longitudinal survey design was used to explore the mediating role of emotion regulation in the relationships between self-transcendence (vs. self-enhancement) values and emotional adjustment among adolescents. Participants were 863 senior school students from Shanghai and Qingdao, Shandong Province. Data on self-transcendence and self-enhancement values, loneliness, depression, and emotion regulation were collected at 2019 and 2020 by using self-report measures. The results showed that (1) adolescents' endorsement with self-transcendence values decreased and self-enhancement values increased; compared to adolescents in Qingdao, adolescents in Shanghai were more depressed, (2) emotion regulation only mediated the effect of self-transcendence values on loneliness, and (3) not only the relationship between self-transcendence values and depression, but also the relationships between self-enhancement values and loneliness and depression were suppressed by emotion regulation. The study may provide more empirical evidences for the benefits of self-transcendence values and may also give more references on how to improve adolescents' emotional adjustment.

CSF-GTNet: A novel multi-dimensional feature fusion network based on Convnext-GeLU-BiLSTM for EEG-signals-enabled fatigue driving detection.

Electroencephalography(EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features. More importantly, most current work only treats deep learning models as classifiers. They ignored the features of different subjects learned by the model. Aiming at the above problems, this paper proposes a novel multi-dimensional feature fusion network, CSF-GTNet, based on time and space-frequency domains for fatigue detection. Specifically, it comprises Gaussian Time Domain Network (GTNet) and Pure Convolutional Spatial Frequency Domain Network (CSFNet). The experimental results show that the proposed method effectively distinguishes between alert and fatigue states. The accuracy rates are 85.16% and 81.48% on the self-made and SEED-VIG datasets, respectively, which are higher than the state-of-the-art methods. Moreover, we analyze the contribution of each brain region for fatigue detection through the brain topology map. In addition, we explore the changing trend of each frequency band and the significance between different subjects in the alert state and fatigue state through the heat map. Our research can provide new ideas in brain fatigue research and play a specific role in promoting the development of this field. The code is available on https://github.com/liio123/EEG_Fatigue.

Reduction of Reactive Oxygen Species Accumulation Using Gadolinium-Doped Ceria for the Alleviation of Atherosclerosis.

Atherosclerosis is a common cardiovascular disease with increasing morbidity and mortality. The pathogenesis of atherosclerosis is strongly related to endothelial dysfunction, which is induced by severe oxidative stress damage derived from reactive oxygen species (ROS). Thus, ROS plays a critical role in the pathogenesis and progression of atherosclerosis. In this work, we demonstrated that the gadolinium doping of CeO2 (Gd/CeO2) nanozymes as effective ROS scavengers delivered high performance for antiatherosclerosis. It was found that the chemical doping of Gd promoted the surface proportion of Ce3+ in the nanozymes and thereby enhanced the overall ROS scavenging ability. In vitro and in vivo experiments unambiguously showed that the Gd/CeO2 nanozymes efficiently scavenged harmful ROS at the cellular and histological levels. Further, Gd/CeO2 nanozymes were demonstrated to significantly reduce vascular lesions by reducing lipid accumulation in macrophage and decreasing inflammatory factor levels, thereby inhibiting the exacerbation of atherosclerosis. Moreover, Gd/CeO2 can serve as T1-weighted magnetic resonance imaging contrast agents, which can generate sufficient contrast to distinguish the location of plaque during living imaging. Through those efforts, Gd/CeO2 may serve as a potential diagnostic and treatment nanomedicine for the ROS-induced atherosclerosis.

Academic performance and depression in Chinese children: Same-domain and cross-domain effects in friendships.

This 1-year longitudinal study examined the effects of academic performance and depression in friendships among elementary school children in China. Participants included 1122 children (44% boys) within 561 stable friendship dyads initially in fourth and fifth grades (initial Mage  = 11 years). Data on academic performance, depression, and friendship were collected from multiple sources in the period of 2012 to 2014. Dyadic analysis using the actor-partner interdependence model showed that friends' academic performance significantly and positively predicted children's later academic performance, indicating same-domain effects, and significantly and negatively predicted children's later depression, indicating cross-domain effects, with a medium effect size (ε3  = .70) for the effects of academic performance. The results help understand the role of friendship in children's development in the Chinese context.

Nitrogen Deposition Amplifies the Legacy Effects of Plant Invasion.

The legacy effects of invasive plant species can hinder the recovery of native communities, especially under nitrogen deposition conditions, where invasive species show growth advantages and trigger secondary invasions in controlled areas. Therefore, it is crucial to thoroughly investigate the effects of nitrogen deposition on the legacy effects of plant invasions and their mechanisms. The hypotheses of this study are as follows: (1) Nitrogen deposition amplifies the legacy effects of plant invasion. This phenomenon was investigated by analysing four potential mechanisms covering community system structure, nitrogen metabolism, geochemical cycles, and microbial mechanisms. The results suggest that microorganisms drive plant-soil feedback processes, even regulating or limiting other factors. (2) The impact of nitrogen deposition on the legacy effects of plant invasions may be intensified primarily through enhanced nitrogen metabolism via microbial anaerobes bacteria. Essential insights into invasion ecology and ecological management have been provided by analysing how nitrogen-fixing bacteria improve nitrogen metabolism and establish sustainable methods for controlling invasive plant species. This in-depth study contributes to our better understanding of the lasting effects of plant invasions on ecosystems and provides valuable guidance for future ecological management.

Altered brain functional activity and connectivity in bone metastasis pain of lung cancer patients: A preliminary resting-state fMRI study.

Bone metastasis pain (BMP) is one of the most prevalent symptoms among cancer survivors. The present study aims to explore the brain functional activity and connectivity patterns in BMP of lung cancer patients preliminarily. Thirty BMP patients and 33 healthy controls (HCs) matched for age and sex were recruited from inpatients and communities, respectively. All participants underwent fMRI data acquisition and pain assessment. Low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) were applied to evaluate brain functional activity. Then, functional connectivity (FC) was calculated for the ALFF- and ReHo-identified seed brain regions. A two-sample t-test or Manny-Whitney U-test was applied to compare demographic and neuropsychological data as well as the neuroimaging indices according to the data distribution. A correlation analysis was conducted to explore the potential relationships between neuroimaging indices and pain intensity. Receiver operating characteristic curve analysis was applied to assess the classification performance of neuroimaging indices in discriminating individual subjects between the BMP patients and HCs. No significant intergroup differences in demographic and neuropsychological data were noted. BMP patients showed reduced ALFF and ReHo largely in the prefrontal cortex and increased ReHo in the bilateral thalamus and left fusiform gyrus. The lower FC was found within the prefrontal cortex. No significant correlation between the neuroimaging indices and pain intensity was observed. The neuroimaging indices showed satisfactory classification performance between the BMP patients and HCs, and the combined ALFF and ReHo showed a better accuracy rate (93.7%) than individual indices. In conclusion, altered brain functional activity and connectivity in the prefrontal cortex, fusiform gyrus, and thalamus may be associated with the neuropathology of BMP and may represent a potential biomarker for classifying BMP patients and healthy controls.

First report of green mold disease caused by Penicillium citrinum on Dictyophora rubrovalvata in China.

Dictyophora rubrovolvata is a saprophytic mushroom widely cultivated in China, including Guizhou Province for its high nutritional, medicinal, and economical values (Chen et al. 2021). In May 2021, green mold disease was observed on the fruiting bodies of D. rubrovolvata, causing its death or preventing it from forming a sporocarp, in an indoor-production facility at Asuo village, Baiyun District Guiyang city, Guizhou Province, China (26°73'51" N, 106°72'88" E). The disease incidence was 60%-70% in the affected 1.33-ha growing area, causing a serious economic loss. To identify the causal agent, a total of 15 samples with symptomatic symptoms were collected. Small pieces (5 mm × 5 mm) were cut from the diseased tissues, surface sterilized in 0.4% NaClO for 5 min, washed three times with sterilized water, placed on potato dextrose agar (PDA) medium, and incubated at 24 °C for 7 days. Twenty-one pure cultures were obtained by single-spore isolation method. The colonies were initially white but after seven days as conidia developed they turned green. Hyphae were hyaline and guttulate. Conidiophores were verrucose stipes, triverticulate, and phialides flask shaped. Conidia were smooth and pale green, with subglobose to globose shape measuring 2.0-2.5 × 1.8-2.5 µm (n=50). Based on these morphological characteristics, the isolates matched the description of the genus Penicillium (Visagie et al. 2014). To confirm the identity, DNA of five representative isolates (QS001, QS005, QS008, QS015, QS017) was extracted according to the manufacturer's instructions (Biomiga Fungal DNA Extraction Kit; CA, USA). Afterwards, PCR was performed to amplify ITS region, calmodulin and ß-tubulin genes using primer pairs ITS1/ITS4 (White et al. 1990), CMD5/CMD6 (Glass et al. 1995), and Bt2a/Bt2b (Hong et al. 2006), respectively. BLASTN analysis of these sequences showed the best matches with Penicillium citrinum CBS 139.45 (ITS region: 98.60% (493/500 bp) identity to accession MH856132.1; CMD: 99.79% (469/470 bp) identity to accession MN969245.1; ß-tubulin:100% (407/407 bp) identity to accession GU944545.1). Representative sequences of the sequenced DNA regions were deposited in GenBank (ITS region: OK446552; CMD: OK492612; ß-tubulin: OK482677). Furthermore, a phylogenetic tree was constructed with MEGA 7 based on the concatenated sequences. Koch's postulates were met to confirm the pathogenicity of the representative isolate (QS001) on D. rubrovolvata. Six discs (5mm×5mm) from actively growing P. citrinum QS001 colonies (5-day-old) were placed on six fruiting bodies of D. rubrovolvata (5-month-old). Mock inoculations were performed using PDA discs only without any fungus. The inoculation sites were wrapped with a sterilized 200-µm nylon mesh. All fruiting bodies were incubated at 23°C ± 2°C under a 0-h/24-h photoperiod and 80% relative humidity (RH) after inoculation. After 14 days, green mold was observed on all P. citrinum QS001 inoculated mushrooms. In contrast, no disease was observed in mock inoculated group. The disease assays were repeated three times. P. citrinum QS001 was isolated from all inoculated D. rubrovolvata and verified via the molecular analysis mentioned above. To the best of our knowledge, this is the first report that P. citrinum causes green mold on D. rubrovalvata in China and further studies should focus on managing this disease to prevent any disease outbreaks.

A prognostic model using the neutrophil-albumin ratio and PG-SGA to predict overall survival in advanced palliative lung cancer.

OBJECTIVE: Inflammation and malnutrition are common in patients with advanced lung cancer undergoing palliative care, and their survival time is limited. In this study, we created a prognostic model using the Inflam-Nutri score to predict the survival of these patients. METHODS: A retrospective cohort study was conducted on 223 patients with advanced, histologically confirmed unresectable lung cancer treated between January 2017 and December 2018. The cutoff values of the neutrophil-albumin ratio (NAR) and Patient-Generated Subjective Global Assessment (PG-SGA) score were determined by the X-tile program. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analysis were performed to identify prognostic factors of overall survival (OS). We then established a nomogram model. The model was assessed by a validation cohort of 72 patients treated between January 2019 and December 2019. The predictive accuracy and discriminative ability were assessed by the concordance index (C-index), a plot of the calibration curve and risk group stratification. The clinical usefulness of the nomogram was measured by decision curve analysis (DCA). RESULTS: The nomogram incorporated stage, supportive care treatment, the NAR and the PG-SGA score. The calibration curve presented good performance in the validation cohorts. The model showed discriminability with a C-index of 0.76 in the training cohort and 0.77 in the validation cohort. DCA demonstrated that the nomogram provided a higher net benefit across a wide, reasonable range of threshold probabilities for predicting OS. The survival curves of different risk groups were clearly separated. CONCLUSIONS: The NAR and PG-SGA scores were independently related to survival. Our prognostic model based on the Inflam-Nutri score could provide prognostic information for advanced palliative lung cancer patients and physicians.

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems.

Production system modeling (PSM) for quality propagation involves mapping the principles between components and systems. While most existing studies focus on the steady-state analysis, the transient quality analysis remains largely unexplored. It is of significance to fully understand quality propagation, especially during transients, to shorten product changeover time, decrease quality loss, and improve quality. In this paper, a novel analytical PSM approach is established based on the Markov model, to explore product quality propagation for transient analysis of serial multi-stage production systems. The cascade property for quality propagation among correlated sequential stages was investigated, taking into account both the status of the current stage and the quality of the outputs from upstream stages. Closed-form formulae to evaluate transient quality performances of multi-stage systems were formulated, including the dynamics of system quality, settling time, and quality loss. An iterative procedure utilizing the aggregation technique is presented to approximate transient quality performance with computational efficiency and high accuracy. Moreover, system theoretic properties of quality measures were analyzed and the quality bottleneck identification method was investigated. In the case study, the modeling error was 0.36% and the calculation could clearly track system dynamics; quality bottleneck was identified to decrease the quality loss and facilitate continuous improvement. The experimental results illustrate the applicability of the proposed PSM approach.

Early Palliative Care in Patients With Non-Small-Cell Lung Cancer: A Randomized Controlled Trial in Southwest China.

PURPOSE: Effective interventions to improve prognosis in non-small-cell lung cancer (NSCLC) are urgently needed. We assessed the effect of the early integration of interdisciplinary palliative care (based on WARM model) for patients with NSCLC on the quality of life (QoL), psychological state, cancer pain and nutritional status. METHODS: In this randomized controlled trial, 120 newly diagnosed NSCLC patients were enrolled and randomly assigned (1:1) to the combined early palliative care (CEPC) group integrated with standard oncologic care or standard oncological care (SC) group. QoL and psychological state were assessed at baseline and at 24 weeks by Functional Assessment of Cancer Therapy-Lung (FACT-L) scale, the Hospital Anxiety and Depression Scale (HADS) and Patient Health Questionnaire-9 (PHQ-9), respectively. Cancer nutritional and pain status were assessed with the use of the Patient-Generated Subjective Global Assessment (PG-SGA) and Numerical Rating Scale (NRS), respectively. The primary outcome was the change in the quality of life, psychological state and nutritional status at 24 weeks. Analysis was by intention to treat. RESULTS: 120 patients were enrolled: 60 in CEPC group (38 completed) and 60 in the SC group (32 completed). CEPC group had a better QoL than SC group (P < .05). In addition, fewer patients in the CEPC group than in the SC group had depressive (P = .005) symptoms. Furthermore, patients in CEPC group had a better nutritional status than SC group (P = .001). CONCLUSION: Among patients with non-small-cell lung cancer, early palliative care led to significant improvements in quality of life, psychological state and nutritional status.

Functional Analysis of Keto-Acid Reductoisomerase ILVC in the Entomopathogenic Fungus Metarhizium robertsii.

Ketol-acid reductoisomerase (ILVC) is the second enzyme in the branched-chain amino acid (BCAA) biosynthesis, which regulates many physiological activities in a variety of organisms from bacteria to fungi and plants. In this work, function mechanisms of ILVC in Metarhizium robertsii Metchnikoff (Hypocreales: Clavicipitaceae) were explored with site-directed mutagenesis, reductase activity assays and transcriptomics analysis. The reductase activity assays showed that ILVC from phytopathogenic fungi exhibited significantly higher activities than those from entomopathogenic fungi but lower than those from yeast. Site-directed mutagenesis and enzymatic activities of MrILVC with different active-site mutants (Arg-113, Ser-118, Asp-152, Asp-260, and Glu-264) confirmed that active sites of MrILVC are conserved with plant and bacterial ILVCs. Deleting MrilvC causes the complete failures of vegetative growth and conidial germination, feeding with branched-chain amino acids (BCAAs) recovers the fungal growth but not conidial germination, while both characteristics are restored when supplemented with yeast extract. Compared to ΔMrilvC cultured in czapek agar (CZA), plenty of genes involved in the biosynthesis of antibiotics and amino acids were up- or down-regulated in the wild type or ΔMrilvC feeding with either BCAAs or yeast extract. Further analysis showed some genes, such as catalase A, participate in mycelial growth and conidial germination was down-regulated in ΔMrilvC from CZA, revealing that MrILVC might control the fungal development by gene regulation and BCAAs or yeast extract could play partial roles of MrILVC. This study will advance our understanding of ILVC function mechanisms in fungi.

Prevalence of Cardiovascular Events and Their Risk Factors in Patients With Chronic Obstructive Pulmonary Disease and Obstructive Sleep Apnea Overlap Syndrome.

Rationale: Chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA) have been identified as independent risk factors for cardiovascular diseases. However, the impact of COPD and OSA overlap syndrome (OS) on cardiovascular outcomes remains to be elucidated. Objective: To determine the prevalence of cardiovascular events and their risk factors in OS patients. Methods: Seventy-four patients who had OS between January 2015 and July 2020 were retrospectively enrolled, and 222 COPD-only patients and 222 OSA-only patients were pair-matched for age and sex from the same period and served as the OS-free control group. The prevalence rates of coronary heart disease (CHD), arrhythmia, heart failure, and pulmonary arterial hypertension (PAH) were compared among the three groups, and multivariable logistic regression models were used to screen the risk factors for specific cardiovascular events. Results: OS patients had higher prevalence rates of heart failure (10.8 vs. 0.5 and 1.4%, respectively) and PAH (31.1 vs. 4.5 and 17.1%, respectively) than those with OSA alone or COPD alone (all P < 0.01). The CHD prevalence was also significantly higher in the OS group than in the COPD-alone group (25.7 vs. 11.7%, P < 0.01). There was no significant difference in the prevalence of arrhythmia among the three groups (20.3, 22.5, and 13.1%, respectively, P > 0.05). In OS patients, risk factors for CHD included hypertension, diabetes, body mass index, lactate dehydrogenase level, and tidal volume; risk factors for heart failure included diabetes, partial pressure of oxygen, partial pressure of carbon dioxide, maximum ventilatory volume, and neutrophilic granulocyte percentage; and risk factors for PAH included minimum nocturnal oxygen saturation, partial pressure of carbon dioxide, and brain natriuretic peptide and lactate dehydrogenase levels. Conclusions: OS patients have a higher prevalence of cardiovascular events, which is associated with hypoxemia, hypercapnia, and impaired lung function in these patients.

Deep Learning of Ultrasound Imaging for Evaluating Ambulatory Function of Individuals with Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) results in loss of ambulation and premature death. Ultrasound provides real-time, safe, and cost-effective routine examinations. Deep learning allows the automatic generation of useful features for classification. This study utilized deep learning of ultrasound imaging for classifying patients with DMD based on their ambulatory function. A total of 85 individuals (including ambulatory and nonambulatory subjects) underwent ultrasound examinations of the gastrocnemius for deep learning of image data using LeNet, AlexNet, VGG-16, VGG-16TL, VGG-19, and VGG-19TL models (the notation TL indicates fine-tuning pretrained models). Gradient-weighted class activation mapping (Grad-CAM) was used to visualize features recognized by the models. The classification performance was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve analysis. The results show that each deep learning model endows muscle ultrasound imaging with the ability to enable DMD evaluations. The Grad-CAMs indicated that boundary visibility, muscular texture clarity, and posterior shadowing are relevant sonographic features recognized by the models for evaluating ambulatory function. Of the proposed models, VGG-19 provided satisfying classification performance (the area under the ROC curve: 0.98; accuracy: 94.18%) and feature recognition in terms of physical characteristics. Deep learning of muscle ultrasound is a potential strategy for DMD characterization.

Knockdown of SNORA47 Inhibits the Tumorigenesis of NSCLC via Mediation of PI3K/Akt Signaling Pathway.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a frequently diagnosed aggressive cancer all over the world. Small nucleolar RNAs (snoRNAs) are a group of non-coding mediatory RNAs. A previous report indicated that small nucleolar RNA 47 (SNORA47) is upregulated in NSCLC. However, the role of SNORA47 in NSCLC is unclear. MATERIAL AND METHODS: Cell proliferation was measured by immunofluorescence staining. Cell apoptosis and cycle of NSCLC were tested by flow cytometry and the protein expressions were investigated by Western-blot. Meanwhile, cell migration and invasion were detected by transwell assay. Xenograft mice model was established to detect the effect of SNORA47 knockdown on tumor growth of NSLC in vivo. RESULTS: Knockdown of SNORA47 significantly inhibited the proliferation of NSCLC cells via inducing cell apoptosis. Moreover, migration and invasion of NSCLC cells were notably decreased by SNORA47 shRNA. SNORA47 knockdown significantly induced G1 arrest in NSCLC cells via regulation of p27 Kip1, CDK2, and cyclin D1. Meanwhile, SNORA47 shRNA inhibited EMT process and PI3K/Akt signaling in NSCLC cells. Finally, silencing of SNORA47 significantly inhibited the tumor growth of NSCLC in vivo. CONCLUSION: Knockdown of SNORA47 significantly inhibited the tumorigenesis of NSCLC via inhibition of PI3K/Akt signaling and EMT process. Thereby, our finding might shed a new light on exploring the strategies for the treatment of NSCLC.