Untargeted metabolomics revealed the effect of soybean metabolites on poly(γ-glutamic acid) production in fermented natto and its metabolic pathway

BACKGROUND

Natto mucus is mainly composed of poly(γ-glutamic acid) (γ-PGA), which affects the sensory quality of natto and has some effective functional activities. The soybean metabolites that cause different γ-PGA contents in different fermented natto are unclear.

RESULTS

In this study, we use untargeted metabolomics to analyze the metabolites of high-production γ-PGA natto and low-production γ-PGA natto and their fermented substrate soybean. A total of 257 main significantly different metabolites with the same trend among the three comparison groups were screened, of which 114 were downregulated and 143 were upregulated. Through the enrichment of metabolic pathways, the metabolic pathways with significant differences were purine metabolism, nucleotide metabolism, fructose and mannose metabolism, anthocyanin biosynthesis, isoflavonoid biosynthesis and the pentose phosphate pathway.

CONCLUSION

For 114 downregulated main significantly different metabolites with the same trend among the three comparison groups, Bacillus subtilis (natto) may directly decompose them to synthesize γ-PGA. Adding downregulated substances before fermentation or cultivating soybean varieties with the goal of high production of such substances has a great effect on the production of γ-PGA by natto fermentation. The enrichment analysis results showed the main pathways affecting the production of γ-PGA by Bacillus subtilis (natto) using soybean metabolites, which provides a theoretical basis for the production of γ-PGA by soybean and promotes the diversification of natto products. © 2023 Society of Chemical Industry.

Association between retinal thickness and disease characteristics in adult epilepsy: A cross-sectional OCT evaluation

OBJECTIVE

Thinning of the peripapillary retinal nerve fiber layer (p-RNFL), as measured by optical coherence tomography (OCT), was recently introduced as a promising marker for cerebral neuronal loss in people with epilepsy (PwE). However, its clinical implication remains to be elucidated. We thus aimed to (1) systematically characterize the extent of the retinal neuroaxonal loss in a broad spectrum of unselected PwE and (2) to evaluate the main clinical determinants.

METHODS

In this prospective study, a spectral-domain OCT evaluation was performed on 98 well-characterized PwE and 85 healthy controls (HCs) (18-55 years of age). All inner retinal layers and the total macula volume were assessed. Group comparisons and linear regression analyses with stepwise backward selection were performed to identify relevant clinical and demographic modulators of the retinal neuroaxonal integrity.

RESULTS

PwE (age: 33.7 ± 10.6 years; 58.2% female) revealed a significant neuroaxonal loss across all assessed retinal layers (global pRNFL, P = 0.001, Δ = 4.24 μm; macular RNFL, P < 0.001, Δ = 0.05 mm3 ; ganglion cell inner plexiform layer, P < 0.001, Δ = 0.11 mm3 ; inner nuclear layer, INL, P = 0.03, Δ = 0.02 mm3 ) as well as significantly reduced total macula volumes (TMV, P < 0.001, Δ = 0.18 mm3 ) compared to HCs (age: 31.2 ± 9.0 years; 57.6% female). The extent of retinal neuroaxonal loss was associated with the occurrence and frequency of tonic-clonic seizures and the number of antiseizure medications, and was most pronounced in male patients.

SIGNIFICANCE

PwE presented an extensive retinal neuroaxonal loss, affecting not only the peripapillary but also macular structures. The noninvasive and economic measurement via OCT bears the potential to establish as a practical tool to inform patient management, as the extent of the retinal neuroaxonal loss reflects aspects of disease severity and sex-specific vulnerability.

PLAIN LANGUAGE SUMMARY

The retina is an extension of the brain and closely connected to it. Thus, cerebral alterations like atrophy reflect also on the retinal level. This is advantageous, as the retina is easily accessible and measureable with help of the optical coherence tomography. Here we report that adults with epilepsy have a significantly thinner retina than healthy persons. Especially people with many big seizures and a lot of medications have a thinner retina. We propose that measurement of the retina can be useful as a marker of disease severity and to inform patient management.

Endocannabinoids, endocannabinoid-like compounds and cortisone in head hair of health care workers as markers of stress and resilience during the early COVID-19 pandemic

The pandemic caused by SARS-CoV-2 impacted health systems globally, creating increased workload and mental stress upon health care workers (HCW). During the first pandemic wave (March to May 2020) in southern Germany, we investigated the impact of stress and the resilience to stress in HCW by measuring changes in hair concentrations of endocannabinoids, endocannabinoid-like compounds and cortisone. HCW (n = 178) recruited from multiple occupation and worksites in the LMU-University-Hospital in Munich were interviewed at four interval visits to evaluate mental stress associated with the COVID-19 pandemic. A strand of hair of up to 6 cm in length was sampled once in May 2020, which enabled retrospective individual stress hormone quantifications during that aforementioned time period. Perceived anxiety and impact on mental health were demonstrated to be higher at the beginning of the COVID-19 pandemic and decreased significantly thereafter. Resilience was stable over time, but noted to be lower in women than in men. The concentrations of the endocannabinoid anandamide (AEA) and the structural congeners N-palmitoylethanolamide (PEA), N-oleoylethanolamide (OEA) and N-stearoylethanolamide (SEA) were noted to have decreased significantly over the course of the pandemic. In contrast, the endocannabinoid 2-arachidonoylglycerol (2-AG) levels increased significantly and were found to be higher in nurses, laboratory staff and hospital administration than in physicians. PEA was significantly higher in subjects with a higher resilience but lower in subjects with anxiety. SEA was also noted to be reduced in subjects with anxiety. Nurses had significantly higher cortisone levels than physicians, while female subjects had significant lower cortisone levels than males. Hair samples provided temporal and measurable objective psychophysiological-hormonal information. The hair endocannabinoids/endocannabinoid-like compounds and cortisone correlated to each other and to professions, age and sex quite differentially, relative to specific periods of the COVID-19 pandemic.

Long Noncoding RNAs MALAT1 and HOTTIP Act as Serum Biomarkers for Hepatocellular Carcinoma

BACKGROUND

Circulating tumor markers with satisfactory sensitivity and specificity play crucial roles in cancer diagnosis and therapy. This prospective study aimed to evaluate the potential of circulating lncRNAs as biomarkers for hepatocellular carcinoma (HCC).

METHODS

A total of 74 patients with HCC and 94 healthy controls were enrolled. The expression levels of candidate genes in serum were detected by qRT-PCR. Receiver operating characteristic (ROC) curve analysis and logistic regression were employed to investigate the diagnostic capacity of lncRNAs. The analysis of 3-year overall survival (OS) was conducted using the Kaplan-Meier method and log-rank test.

RESULTS

Of the 9 candidate genes, 6 lncRNAs could be stably detected in serum. The expression levels of circulating MALAT1 and HOTTIP in HCC patients were significantly higher than those in controls (P < 0.001). ROC analysis showed that MALAT1 and HOTTIP were more effective than alpha-fetoprotein (AFP) (P < 0.010) in the diagnosis of HCC, with AUCs of 0.896 and 0.899, respectively. Additionally, a panel consisting of MALAT1, HOTTIP, and AFP was constructed to obtain an AUC of 0.968 with a sensitivity of 87.8% and specificity of 94.7% in HCC diagnosis. Moreover, the upregulation of MALAT1 was not only related to multiple tumor lesions, HCV infection, AST level, and AFP level, but also suggested shorter OS. A high expression level of HOTTIP was associated with metastasis.

CONCLUSION

Serum MALAT1 and HOTTIP play indicative roles as non-invasive biomarkers for HCC.

Managing intermediate phase transition of perovskite film with gearbox-like molecule for efficient and stable solar cells

The smooth and dense light-absorbing layer is an essential factor in polycrystalline solar cells to achieve high photovoltaic performance, while it remains challenging in perovskite solar cells because of the difficulty balancing the speed of crystal nucleation and growth in a solution way. Here, we explored a perovskite nucleation/growth compatible model via manipulating the intermediate complex induced by n-hexylamine (NHA) molecule, guiding us to adjustments perovskite nucleation and growth process. We found that the NHA can act as a gearbox-like molecule to sequentially reduce the perovskite nucleation barrier, promote the nucleation velocity, and retard the perovskite growth simultaneously to obtain uniform perovskite films; correspondingly, this modulation also yields the buried interface with fewer voids and low defects density. In addition, the hydrophobic NHA with long alkyl chain improves the moisture tolerance of the perovskite. The treated solar cell power conversion efficiency was 21.91 %. Importantly, in ∼ 70 % humidity at 25 °C for 30 days, the efficiency of the device declined less than 5 %, exhibiting a good stability performance.

Aerodynamic performance of flapping wing with alula under different kinematics of complex flapping motion

The flight of birds is a remarkable feat, and their remarkable ability to fly derives from complex multi-degree-of-freedom flapping motions and small-scale feather structures that have evolved over millions of years. One of these feather structures is the alula, which can enhance the birds' flight performance at low speeds and large angles of attack. Previous studies on the alula have focused on the steady state. This undoubtedly ignores the unsteady effect caused by complex flapping motion, which is also the most important characteristic of avian flight. Therefore, this paper carries out a study on the effect of different motion modes and motion parameters on the aerodynamic mechanism of the alula. Previous studies found the dominate effect in the lift enhancement is influenced by Reynolds number, stall condition and geometric parameters. After coupling complex flapping motion, aerodynamic characteristics of the flapping wing are greatly influenced by different motion patterns and parameters. For pure plunge motion, both the slot effect and the vortex generator effect of the alula dominate the lift enhancement; while for plunge-twist and plunge-sweep motion, the vortex generator dominates more. At a low plunge amplitude, a low twist amplitude and a low sweep amplitude, the deflection of the alula has a good lift enhancement compared with the baseline wing. Increasing these amplitudes attenuates both the slot effect and the vortex generator effect. The alula can enhance the lift by 10.4% at the plunge amplitude of 25 deg (for pure plunge motion), by 7.9% at the plunge amplitude of 25 deg and twist amplitude of 10 deg (for plunge-twist motion), by 3.3% at the plunge amplitude of 25 deg and sweep amplitude of 15 deg (for plunge-sweep motion). Meanwhile, at a large sweep phase angle, the alula has a better lift enhancement. Increasing the phase angle enhances the vortex generator effect of the alula, and it has an optimal lift enhancement effect of 11% at the phase angle of 180 deg.

Specificity in the processing of a subject's own name

Subject's own name (SON) is widely used in both daily life and the clinic. Event-related potential (ERP)-based studies have previously detected several ERP components related to SON processing; however, as most of these studies used SON as a deviant stimulus, it was not possible to determine whether these components were SON-specific. To identify SON-specific ERP components, we adopted a passive listening task with EEG data recording involving 25 subjects. The auditory stimuli were a SON, a friend's name (FN), an unfamiliar name (UN) selected from other subjects' names and seven different unfamiliar names (DUNs). The experimental settings included Equal-probabilistic, Frequent-SON, Frequent-FN and Frequent-UN conditions. The results showed that SON consistently evoked a frontocentral SON-related negativity (SRN) within 210-350 ms under all conditions, which was not detected with the other names. Meanwhile, a late positive potential evoked by SON was found to be affected by stimulus probability, showing no significant difference between the SON and the other names in the Frequent-SON condition, or between the SON and a FN in the Frequent-UN condition. Taken together, our findings indicated that the SRN was a SON-specific ERP component, suggesting that distinct neural mechanism underly the processing of a SON.

Machine-Learning-Assisted Procoagulant Extracellular Vesicle Barcode Assay toward High-Performance Evaluation of Thrombosis-Induced Death Risk in Cancer Patients

Venous thromboembolism (VTE) is the most fatal complication in cancer patients. Unfortunately, the frequent misdiagnosis of VTE owing to the lack of accurate and efficient evaluation approaches may cause belated medical intervention and even sudden death. Herein, we present a rapid, easily operable, highly specific, and highly sensitive procoagulant extracellular vesicle barcode (PEVB) assay composed of TiO2 nanoflower (TiNFs) for visually evaluating VTE risk in cancer patients. TiNFs demonstrate rapid label-free EV capture capability by the synergetic effect of TiO2-phospholipids molecular interactions and topological interactions between TiNFs and EVs. From ordinary plasma samples, the PEVB assay can evaluate potential VTE risk by integrating TiNFs-based EV capture and in situ EV procoagulant ability test with machine-learning-assisted clinical data analysis. We demonstrate the feasibility of this PEVB assay in VTE risk evaluation by screening 167 cancer patients, as well as the high specificity (97.1%) and high sensitivity (96.8%), fully exceeding the nonspecific and posterior traditional VTE test. Together, we proposed a TiNFs platform allowing for highly accurate and timely diagnosis of VTE in cancer patients.

Exosome-loaded degradable polymeric microcapsules for the treatment of vitreoretinal diseases

The therapeutic benefits of many cell types involve paracrine mechanisms. Inspired by the paracrine functions of exosomes and the sustained degradation properties of microcapsules, here we report the therapeutic benefits of exosome-loaded degradable poly(lactic-co-glycolic acid) microcapsules with micrometric pores for the treatment of vitreoretinal diseases. On intravitreal injection in a mouse model of retinal ischaemia-reperfusion injury, microcapsules encapsulating mouse mesenchymal-stem-cell-derived exosomes settled in the inferior vitreous cavity, released exosomes for over one month as they underwent degradation and led to the restoration of retinal thickness to nearly that of the healthy retina. In mice and non-human primates with primed mycobacterial uveitis, intravitreally injected microcapsules loaded with exosomes from monkey regulatory T cells resulted in a substantial reduction in the levels of inflammatory cells. The exosome-encapsulating microcapsules, which can be lyophilised, may offer alternative treatment options for vitreoretinal diseases.

Use of Nanoindentation in Determination of Regional Biomechanical Properties of Rabbit Cornea After UVA Cross-Linking

Purpose

To evaluate the regional effects of different corneal cross-linking (CXL) protocols on corneal biomechanical properties.

Methods

The study involved both eyes of 50 rabbits, and the left eyes were randomized to the five intervention groups, which included the standard CXL group (SCXL), which was exposed to 3-mW/cm2 irradiation, and three accelerated CXL groups (ACXL1-3), which were exposed to ultraviolet-A at irradiations of 9 mW/cm2, 18 mW/cm2, and 30 mW/cm2, respectively, but with the same total dose (5.4 J/cm2). A control (CO) group was not exposed to ultraviolet-A. No surgery was done on the contralateral eyes. The corneas of each group were evaluated by the effective elastic modulus (Eeff) and the hydraulic conductivity (K) within a 7.5-mm radius using nanoindentation measurements.

Results

Compared with the CO group, Eeff (in regions with radii of 0-1.5 mm, 1.5-3.0 mm, and 3.0-4.5 mm) significantly increased by 309%, 276%, and 226%, respectively, with SCXL; by 222%, 209%, and 173%, respectively, with ACXL1; by 111%, 109%, and 94%, respectively, with ACXL2; and by 59%, 41%, and 37%, respectively, with ACXL3 (all P < 0.05). K was also significantly reduced by 84%, 81%, and 78%, respectively, with SCXL; by 75%, 74%, and 70%, respectively, with ACXL1; by 64%, 62%, and 61%, respectively, with ACXL2; and by 33%, 36%, and 32%, respectively, with ACXL3 (all P < 0.05). For the other regions(with radii between 4.5 and 7.5 mm), the SCXL and ACXL1 groups (but not the ACXL2 and ACXL3 groups) still showed significant changes in Eeff and K.

Conclusions

CXL had a significant effect on corneal biomechanics in both standard and accelerated procedures that may go beyond the irradiated area. The effect of CXL in stiffening the tissue and reducing permeability consistently decreased with reducing the irradiance duration.

Prediction of plant secondary metabolic pathways using deep transfer learning

BACKGROUND

Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways.

RESULTS

GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids.

CONCLUSIONS

The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

Research on carbon footprint measurement and emissions reduction optimization of the beer supply chain in China

The carbon emissions of the beer industry have always been a concern. To reduce environmental pollution, this paper systematically calculates the carbon emissions of the beer supply chain and studies the optimization of carbon emissions reduction through scenario simulation. In this study, the boundary of the beer supply chain carbon emissions system is constructed. The scenario simulation and simulation analysis are carried out based on five factors: water recycling utilization rate, power generation mode, weight-volume ratio, recovery ratio, and transportation mode. To obtain the optimal scheme of economic cost and emissions reduction, the cost of the beer supply chain is analyzed under the condition of carbon trading. The results show that the proportion of carbon emissions in each link of the beer supply chain is production, packaging, transportation, and storage, which are 59.58%, 22.27%, 17.67%, and 0.5%, respectively. The improvement of the water recycling utilization rate of manufacturing enterprises can reduce the overall carbon emissions of the supply chain, and the effect is obvious. The highest proportion of carbon emissions in the packaging link is the bottle consumption stage (22.43%). Raw materials transportation accounts for the highest proportion of total carbon emissions in transportation (63.73%). The waterway/railway ratio will be increased to 0.6/0.4, and the carbon emissions in the supply chain will be reduced by 5.7%. In the context of carbon trading, when the carbon trading price is higher than 49.16 yuan/ton, enterprises can achieve a win-win situation in reducing emissions and maintaining positive economic growth by the measures given in the paper.

NeuroD4 converts glioblastoma cells into neuron-like cells through the SLC7A11-GSH-GPX4 antioxidant axis

Cell fate and proliferation ability can be transformed through reprogramming technology. Reprogramming glioblastoma cells into neuron-like cells holds great promise for glioblastoma treatment, as it induces their terminal differentiation. NeuroD4 (Neuronal Differentiation 4) is a crucial transcription factor in neuronal development and has the potential to convert astrocytes into functional neurons. In this study, we exclusively employed NeuroD4 to reprogram glioblastoma cells into neuron-like cells. In vivo, the reprogrammed glioblastoma cells demonstrated terminal differentiation, inhibited proliferation, and exited the cell cycle. Additionally, NeuroD4 virus-infected xenografts exhibited smaller sizes compared to the GFP group, and tumor-bearing mice in the GFP+NeuroD4 group experienced prolonged survival. Mechanistically, NeuroD4 overexpression significantly reduced the expression of SLC7A11 and Glutathione peroxidase 4 (GPX4). The ferroptosis inhibitor ferrostatin-1 effectively blocked the NeuroD4-mediated process of neuron reprogramming in glioblastoma. To summarize, our study demonstrates that NeuroD4 overexpression can reprogram glioblastoma cells into neuron-like cells through the SLC7A11-GSH-GPX4 signaling pathway, thus offering a potential novel therapeutic approach for glioblastoma.

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Three-dimensional memristive Morris-Lecar model with magnetic induction effects and its FPGA implementation

To characterize the magnetic induction flow induced by neuron membrane potential, a three-dimensional (3D) memristive Morris-Lecar (ML) neuron model is proposed in this paper. It is achieved using a memristor induction current to replace the slow modulation current in the existing 3D ML neuron model with fast-slow structure. The magnetic induction effects on firing activities are explained by the spiking/bursting firings with period-adding bifurcation and periodic/chaotic spiking-bursting patterns, and the bifurcation mechanisms of the bursting patterns are elaborated using the fast-slow analysis method to create two bifurcation sets. In particular, the 3D memristive ML model can also exhibit the homogeneous coexisting bursting patterns when switching the memristor initial states, which are effectively illustrated by the theoretical analysis and numerical simulations. Finally, a digitally FPGA-based hardware platform is developed for the 3D memristive ML model and the experimentally measured results well verify the numerical ones.

Retinal and choroidal microvascular characterization and density changes in different stages of diabetic retinopathy eyes

Background

The purpose of this study was to evaluate the changes in fundus vascular density and micromorphological structure of all vascular plexuses during the different stages of diabetic retinopathy (DR), and the correlation between fundus blood flow and the DR severity.

Methods

This observational cross-sectional study was conducted of 50 eyes with different stages of DR, 25 diabetes mellitus (DM) patients without clinical signs of DR and 41 healthy eyes. The foveal avascular zone (FAZ), vessel density of superficial capillary plexus (SCP), and deep retinal capillary plexus (DCP) were acquired by RTVue XR Avanti OCTA device. The perfusion density (PD), skeleton vessel density (SVD) was manually calculated using ImageJ. The area under receiver operating characteristic (ROC) curve was used to determine the diagnostic value of OCTA parameters in distinguishing DR and healthy eyes.

Results

The choroidal VD were significantly higher in the healthy group than in the DM without DR, NPDR, and PDR groups (p < 0.001). The mean retinal parafovea VD, PD, and retinal SVD were higher in healthy and DM without DR eyes compared with NPDR and DR eyes in all vascular layers (p < 0.001). The parafoveal VD of SCP, and DCP decreased, and FAZ area increased with the exacerbation of DR. The OCTA parameters, including FAZ area, parafovea VD, PD, and SVD in all vascular layers showed significant correlation with DR severity (all p < 0.001). ROC curves of OCTA parameters (FAZ area, retinal parafovea VD, retinal PD, and SVD in all vascular layers) for had high sensitivity and specificity in distinguishing DR versus healthy eyes.

Conclusion

The choroidal parafovea VD, retinal parafovea VD, retinal PD, and SVD in the two plexuses decreased, and retinal FAZ area increased significantly with worsening DR. VD, PD, and SVD might be potential early biomarkers indicating the progression of DR before appearance of clinically PDR in patients with DM. In this study, OCTA parameters had high sensitivity and specificity in distinguishing DR and healthy eyes.

Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Memristor-enabled in-memory computing provides an unconventional computing paradigm to surpass the energy efficiency of von Neumann computers. Owing to the limitation of the computing mechanism, while the crossbar structure is desirable for dense computation, the system's energy and area efficiency degrade substantially in performing sparse computation tasks, such as scientific computing. In this work, we report a high-efficiency in-memory sparse computing system based on a self-rectifying memristor array. This system originates from an analog computing mechanism that is motivated by the device's self-rectifying nature, which can achieve an overall performance of ~97 to ~11 TOPS/W for 2- to 8-bit sparse computation when processing practical scientific computing tasks. Compared to previous in-memory computing system, this work provides over 85 times improvement in energy efficiency with an approximately 340 times reduction in hardware overhead. This work can pave the road toward a highly efficient in-memory computing platform for high-performance computing.

Mutational landscape of SWI/SNF complex genes reveal correlation to predictive biomarkers for immunotherapy sensitivity in lung adenocarcinoma patients

BACKGROUND

The search for prognostic biomarkers indicating sensitivity to immunotherapy in lung adenocarcinoma patients has zeroed in on genes in the switch/sucrose non-fermentable (SWI/SNF) pathway. The mutational profiles of key genes are not clearly defined, however, and no comparisons have been conducted on whether mutations in the genes involved provide the same predictive value.

METHODS

In this study, analysis of clinical factors, tumor mutation burden (TMB), chromosomal instability, and co-alterations was conducted for 4344 lung adenocarcinoma samples. Independent online cohorts (N = 1661 and 576) were used to supplement the analysis with survival and RNA-seq data.

RESULTS

Mutational burden and chromosomal instability analysis showed that ARID family mutations (including ARID1A, ARID1B, or ARID2 mutations) and SMARC family mutations (including SMARCA4 or SMARCB1 mutations) display different profiles from wild-type (WT) samples (TMB: ARID versus WT: P < 2.2 × 10^-16, SMARC versus WT: P < 2.2 × 10^-16; CIN: ARID versus WT: P = 1.8 × 10^-5, SMARC versus WT: P = 0.027). Both mutant groups have a higher proportion of transversions than transitions, whereas the ratio is more equal for wild-type samples. Survival analysis shows that patients with ARID mutations were more sensitive to immunotherapy treatment than wild-type and SMARC-mutated patients (P < 0.001 and P = 0.013, respectively), and multivariate Cox analysis reveals that the presence of ARID mutations is likely the main cause.

CONCLUSIONS

The research presented in this study shows that mutations in the ARID gene family, including ARID1A, ARID1B, and ARID2, are primarily responsible for the sensitive response to immunotherapy treatment in patients with lung adenocarcinoma.

Extreme multistability and phase synchronization in a heterogeneous bi-neuron Rulkov network with memristive electromagnetic induction

Memristive electromagnetic induction effect has been widely explored in bi-neuron network with homogeneous neurons, but rarely in bi-neuron network with heterogeneous ones. This paper builds a bi-neuron network by coupling heterogeneous Rulkov neurons with memristor and investigates the memristive electromagnetic induction effect. Theoretical analysis discloses that the bi-neuron network possesses a line equilibrium state and its stability depends on the memristor coupling strength and initial condition. That is, the stability of the line equilibrium state has a transition between unstable saddle-focus and stable node-focus via Hopf bifurcation. By employing parameters located in the stable node-focus region, dynamical behaviors related to the memristor coupling strength and initial conditions are revealed by Julia- and MATLAB-based multiple numerical tools. Numerical results demonstrate that the proposed heterogeneous bi-neuron Rulkov network can generate point attractor, period, chaos, chaos crisis, and period-doubling bifurcation. Note that extreme multistability are disclosed with respect to initial conditions of memristor and gated ion concentration. Coexisting infinitely multiple firing patterns of periodic firing patterns with different periodicities and chaotic firing patterns for different memristor initial conditions are demonstrated by phase portrait and time-domain waveform. Besides, the phase synchronization related to the memristor coupling strength and its initial condition is explored, which suggests that the two heterogeneous neurons become phase synchronization with large memristor coupling strength and initial condition. This also reflects that the plasticity of memristor synapse enables adaptive regulation in keeping energy balance between the neurons. What's more, MCU-based hardware experiments are executed to further confirm the numerical simulations.

A novel deformable liposomal hydrogel loaded with a SREBP-1-inhibiting polypeptide for reducing sebum synthesis in golden hamster model

Excessive sebum is the major factor involved in the pathophysiology of seborrheic diseases. Chemical medicines can result in mild to severe side effects. Polypeptides with much less side effects make them ideal for reducing sebum synthesis. Sterol regulatory element-binding proteins-1 (SREBP-1) is necessary for the biosynthesis of sterols. A SREBP-1-inhibiting polypeptide (SREi), which competitively inhibits the ubiquitination of Insig-1 so as to suppress the activation of SREBP-1 was selected as an active ingredient and formulated into skin topical preparations. The SREi anionic deformable liposomes contained sodium deoxycholate (SDCh) at the concentration of 4.4 mg/mL (SREi-ADL3) and SREi-ADL3 in 0.3% (w/v) carbomer hydrogel (SREi-ADL3-GEL) were prepared and characterized. The SREi-ADL3 presented a high entrapment efficiency of 92.62 ± 6.32%, a particle size of 99.54 ± 7.56 nm and a surface charge of -19.18 ± 0.45 mV. SREi-ADL3-GEL exhibited a sustained release behavior, a higher stability, a much more cellular uptake ability and transdermal absorption. In vivo golden hamster model confirmed that SREi-ADL3-GEL presented the strongest inhibitory effect on sebaceous gland growth and sebum synthesis by down-regulating the mRNA and protein expression of SREBP-1, fatty acid synthase (FAS) and acetyl-coenzyme A carboxylase 1 (ACC1). As confirmed by histological analysis, only a small amount of sebaceous gland lobes with the lightest staining intensity and the smallest dyeing area could be observed in the SREi-ADL3-GEL group. Taken together, SREi-ADL3-GEL displayed potential applications in sebum excessive production related diseases.

Brain State Relays Self-Processing and Heartbeat-Evoked Cortical Responses

The self has been proposed to be grounded in interoceptive processing, with heartbeat-evoked cortical activity as a neurophysiological marker of this processing. However, inconsistent findings have been reported on the relationship between heartbeat-evoked cortical responses and self-processing (including exteroceptive- and mental-self-processing). In this review, we examine previous research on the association between self-processing and heartbeat-evoked cortical responses and highlight the divergent temporal-spatial characteristics and brain regions involved. We propose that the brain state relays the interaction between self-processing and heartbeat-evoked cortical responses and thus accounts for the inconsistency. The brain state, spontaneous brain activity which highly and continuously changes in a nonrandom way, serves as the foundation upon which the brain functions and was proposed as a point in an extremely high-dimensional space. To elucidate our assumption, we provide reviews on the interactions between dimensions of brain state with both self-processing and heartbeat-evoked cortical responses. These interactions suggest the relay of self-processing and heartbeat-evoked cortical responses by brain state. Finally, we discuss possible approaches to investigate whether and how the brain state impacts the self-heart interaction.

Target-Responsive Metal-Organic Framework Nanosystem with Synergetic Sensitive Detection and Controllable Degradation against the Pesticide Triazophos in Contaminated Samples for Environment Assessment and Food Safety

Developing sensitive practical sensors for monitoring pesticide residues in edible foods and environmental samples is vital for food safety and environmental protection. Enzyme-inhibited biosensors offer effective alternative sensing strategies by using the inherent characteristics of pesticides. To further improve the degradation function of pesticide sensors, here, a target-triggered porphyrin metal-organic framework (MOF)-based nanosystem was designed with the synergetic bifunction of sensitive detection and controllable degradation of the triazophos pesticide. As a result of triazophos-inhibited glutathione consumption, the MOF collapsed and released the ligand porphyrin, leading to the recovery of fluorescence and photosensitization of the free porphyrin. The fluorescence recovery resulted in a sensitive detection limit of 0.6 ng mL^-1 for triazophos, which was also applied for the determination of contaminated samples and bioaccumulation in rice. Furthermore, the target-activated photocatalytic ability of porphyrin endowed the system with the ability to effectively generate reactive oxygen species for degrading triazophos with a removal rate of ∼85%, achieving eco-friendly synergetic detection and photodegradation in a controllable way. Therefore, the intelligent multifunctional MOF system demonstrated the potential of programmable systems for jointly controllable tracking and elimination of pesticide residues in the environment and opened a new avenue for designing a precise mechanism for stimulus-triggered degradation of pesticide residues accompanied by sensitive detection for environmental friendliness and food safety.

A novel estrogen-targeted PEGylated liposome co-delivery oxaliplatin and paclitaxel for the treatment of ovarian cancer

Ovarian cancer is the second cause of death among gynecological malignancies. In this study, we designed a novel estrogen-targeted PEGylated liposome loaded with oxaliplatin and paclitaxel (ES-SSL-OXA/PTX) which could target estrogen receptor (ER) highly expressed on the surface of SKOV-3 cells to enhance therapeutic efficacy and reduce the side effects for SKOV-3 tumor therapy. ES-SSL-OXA/PTX was prepared by thin film hydration method and exhibited a uniform spherical morphology. Encapsulation efficiency (EE) were determined by HPLC method with the results of 44.10% for OXA and 65.85% for PTX. The mean particle size and polydispersity index (PDI) were 168.46 nm and 0.145, respectively. In vivo and in vitro targeting study confirmed that ES-SSL-OXA/PTX has optimum specific targeting ability. Meanwhile, In vitro and in vivo antitumor results of ES-SSL-OXA/PTX exhibited a superior antiproliferative effect on SKOV-3 cells and a stronger anti-tumor efficacy with the tumor inhibition rate of 85.24%. The pharmacokinetics results of ES-SSL-OXA/PTX showed a prolonged half-life time and a slowed clearance rate. The preliminary safety study of acute toxicity and long-term toxicity demonstrated ES-SSL-OXA/PTX exhibited a reduced toxicity profile. Based on the above results, ES-SSL-OXA/PTX could be a promising novel formulation for the treatment of ovarian cancer in future clinic.

Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence

BACKGROUND

The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis.

METHODS

Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs.

RESULTS

The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters.

CONCLUSION

Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.

Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence

BACKGROUND

The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis.

METHODS

Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs.

RESULTS

The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters.

CONCLUSION

Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.