Early identification of stroke through deep learning with multi-modal human speech and movement data.

JOURNAL/nrgr/04.03/01300535-202501000-00031/figure1/v/2024-05-14T021156Z/r/image-tiff Early identification and treatment of stroke can greatly improve patient outcomes and quality of life. Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale (CPSS) and the Face Arm Speech Test (FAST) are commonly used for stroke screening, accurate administration is dependent on specialized training. In this study, we proposed a novel multimodal deep learning approach, based on the FAST, for assessing suspected stroke patients exhibiting symptoms such as limb weakness, facial paresis, and speech disorders in acute settings. We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements, facial expressions, and speech tests based on the FAST. We compared the constructed deep learning model, which was designed to process multi-modal datasets, with six prior models that achieved good action classification performance, including the I3D, SlowFast, X3D, TPN, TimeSformer, and MViT. We found that the findings of our deep learning model had a higher clinical value compared with the other approaches. Moreover, the multi-modal model outperformed its single-module variants, highlighting the benefit of utilizing multiple types of patient data, such as action videos and speech audio. These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke, thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Plant diversity decreases greenhouse gas emissions by increasing soil and plant carbon storage in terrestrial ecosystems.

The decline in global plant diversity has raised concerns about its implications for carbon fixation and global greenhouse gas emissions (GGE), including carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4). Therefore, we conducted a comprehensive meta-analysis of 2103 paired observations, examining GGE, soil organic carbon (SOC) and plant carbon in plant mixtures and monocultures. Our findings indicate that plant mixtures decrease soil N2O emissions by 21.4% compared to monocultures. No significant differences occurred between mixtures and monocultures for soil CO2 emissions, CH4 emissions or CH4 uptake. Plant mixtures exhibit higher SOC and plant carbon storage than monocultures. After 10 years of vegetation development, a 40% reduction in species richness decreases SOC content and plant carbon storage by 12.3% and 58.7% respectively. These findings offer insights into the intricate connections between plant diversity, soil and plant carbon storage and GGE-a critical but previously unexamined aspect of biodiversity-ecosystem functioning.

Large-pore covalent organic framework as solid phase extraction absorbentforefficientdetermination of polypeptide antibiotics in animal-derived foods.

The precise determination of polypeptide antibiotics (PPTs) in foods has been always challenging because of the interference of various endogenous peptides in complex matrix. Herin, a novel large-pore covalent organic framework (TABPT-SPDA-COF) with accessible pore size of 7.9 nm was synthesized as a solid phase extraction (SPE) absorbent for efficiently enriching four PPTs existed in foods originating from animals. The parameters of SPE process were systematically optimized. Subsequently, four PPTs were determined by UHPLC-MS/MS. Under the optimal conditions, TABPT-SPDA-COF shows outstanding enrichment capacity for PPTs in contrast to commercial absorbents ascribed to size selectivity and multiple interaction effects. The method exhibits excellent linear range (0.005-100 ng mL-1), satisfactory limits of detection (0.1 pg mL-1) as well as relative recoveries (86.2-116 %). This work offers a practicable platform to monitor trace PPTs from complex animal-derived foodstuffs.

[Personalized glycemic management for patients with diabetic ketoacidosis based on machine learning].

OBJECTIVE: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment effects using machine learning techniques based on the United States Critical Care Medical Information Mart for Intensive Care- IV (MIMIC- IV). METHODS: Utilizing the MIMIC- IV database, the case data of 2 096 patients with DKA admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center from 2008 to 2019 were analyzed. Machine learning models were developed, and receiver operator characteristic curve (ROC curve) and precision-recall curve (PR curve) were plotted to evaluate the model's effectiveness in predicting four common adverse outcomes: hypoglycemia, hypokalemia, reductions in Glasgow coma scale (GCS), and extended hospital stays. The risk of adverse outcomes was analyzed in relation to the rate of blood glucose decrease. Univariate and multivariate Logistic regression analyses were conducted to examine the relationship between relevant factors and the risk of hypokalemia. Personalized risk interpretation methods and predictive technologies were applied to individualize the analysis of optimal glucose control ranges for patients. RESULTS: The machine learning models demonstrated excellent performance in predicting adverse outcomes in patients with DKA, with areas under the ROC curve (AUROC) and 95% confidence interval (95%CI) for predicting hypoglycemia, hypokalemia, GCS score reduction, and extended hospital stays being 0.826 (0.803-0.849), 0.850 (0.828-0.870), 0.925 (0.903-0.946), and 0.901 (0.883-0.920), respectively. Analysis of the relationship between the rate of blood glucose reduction and the risk of four adverse outcomes showed that a maximum glucose reduction rate > 6.26 mmol×L-1×h-1 significantly increased the risk of hypoglycemia (P < 0.001); a rate > 2.72 mmol×L-1×h-1 significantly elevated the risk of hypokalemia (P < 0.001); a rate > 5.53 mmol×L-1×h-1 significantly reduced the risk of GCS score reduction (P < 0.001); and a rate > 8.03 mmol×L-1×h-1 significantly shortened the length of hospital stay (P < 0.001). Multivariate Logistic regression analysis indicated significant correlations between maximum bicarbonate levels, blood urea nitrogen levels, and total insulin doses with the risk of hypokalemia (all P < 0.01). In terms of establishing personalized optimal treatment thresholds, assuming optimal glucose reduction thresholds for hypoglycemia, hypokalemia, GCS score reduction, and extended hospital stay were x1, x2, x3, x4, respectively, the recommended glucose reduction rates to minimize the risks of hypokalemia and hypoglycemia should be ≤min{x1, x2}, while those to reduce GCS score decline and extended hospital stay should be ≥ max{x3, x4}. When these ranges overlap, i.e., max{x3, x4} ≤ min{x1, x2}, this interval was the recommended optimal glucose reduction range. If there was no overlap between these ranges, i.e., max{x3, x4} > min{x1, x2}, the treatment strategy should be dynamically adjusted considering individual differences in the risk of various adverse outcomes. CONCLUSIONS: The machine learning models shows good performance in predicting adverse outcomes in patients with DKA, assisting in personalized blood glucose management and holding important clinical application prospects.

The burden of non-alcoholic fatty liver disease among working-age people in the Western Pacific Region, 1990-2019: an age-period-cohort analysis of the Global Burden of Disease study.

BACKGROUND: The growing prevalence of non-alcoholic fatty liver disease (NAFLD) in younger populations, particularly those of working age (15-64 years), has become a public health concern. Being diagnosed at a younger age implies a greater likelihood of accruing disability-adjusted life years (DALYs) later in life due to potential progression to conditions such as cirrhosis or hepatocellular carcinoma. This study aims to analyze NAFLD prevalence trends over three decades globally, regionally, and nationally, with a focus on age, period, and birth cohort associations. METHODS: Global, regional, and country time trends in the prevalence of NAFLD among working-age people from 1990 to 2019: Age-period-cohort analysis based on Global Burden of Disease Study 2019 estimates and 95% uncertainty interval (UI) of NAFLD prevalence in the working age population was extracted from the Global Burden of Diseases, Injuries and Risk Factors Study 2019. Age-period-cohort models were used to estimate the prevalence within each age group from 1990 to 2019 (local drift, % per year), fitted longitudinal age-specific rates adjusted for period bias (age effect), and period/cohort relative risk (period/cohort effect). RESULTS: The global age-standardized prevalence (ASPR) of NAFLD increased significantly from 1990 (14,477.6 per 100 000) to 2019 (19,837.6 per 100 000). In the Western Pacific, there were 42,903.8 NAFLD cases in 2019, 54.15% higher than in 1990. The ASPR also increased significantly in the region over the past three decades. At the national level, Palau had the highest ASPR while Brunei Darussalam had the lowest. Age-period-cohort analysis showed that in the Western Pacific, unlike globally, the risk of NAFLD declined after age 60-64 years. Relative to 1980-1989, incidence and DALY risks decreased but prevalence increased in subsequent birth cohorts. Future predictions indicate an upward trend in NAFLD burden, especially among women and medium (SDI) regions like China. CONCLUSION: Non-alcoholic fatty liver disease imparts an immense health burden that continues to grow globally and in the Asia Pacific region. Our work highlights working age adults as an at-risk group and calls attention to socioeconomic gradients within Western Pacific countries. Upward future projections demonstrate that NAFLD prevention is an urgent priority.

Access to essential and innovative anti-cancer medicines: a longitudinal study in Nanjing, China.

PURPOSE: To evaluate the availability, cost, affordability of anti-cancer medicines in Nanjing, Jiangsu. METHODS: A longitudinal tracking investigation study was performed to collect information about 24 essential anti-cancer medicines (EAMs) and 17 innovative anti-cancer medicines (IAMs) in 26 healthcare institutions in Nanjing from 2016 to 2020. The availability, cost, drug utilization and affordability of EAMs and IAMs were investigated. RESULTS: The availability of EAMs showed no significant changes in Nanjing, but the availability of IAMs showed a significant increase in 2018 and 2019 and tended to stabilize in 2020. For EAMs, the DDDc(Defined Daily Dose cost) of LPGs (Lowest-Priced Generics) showed no significant changes, and the DDDc of OBs (Originator Brands) and IAMs significantly decreased. The DDDs(Defined Daily Doses) of EAMs (LPGs) showed a decreasing trend since 2016 and rose again in 2019. Overall, the DDDs of EAMs (LPGs) decreased by 25.18% between 2016 and 2020, but the proportion selected for clinical treatment remained at 67.35% in 2020. The DDDs of EAMs (OBs) and IAMs both showed an increasing trend year by year, with a proportional increase of 207.72% and 652.68%, respectively; but the proportion selected for clinical treatment was only 16.09% and 16.56% respectively in 2020. EAMs (LPGs) had good affordability for urban residents but poor affordability for rural residents; the affordability of EAMs (OBs) and IAMs was poor for both urban and rural residents. CONCLUSIONS: There were no significant changes in the availability and cost of EAMs (LPGs), whose lower prices showed better affordability. Although their relative change in drug utilization showed a decreasing trend, they still dominated clinical treatment. Driven by the national drug price negotiation (NDPN) policy, the availability of IAMs was on the rise. It is necessary to further develop and strengthen policies for essential medicines procurement assessment to improve the accessibility of EAMs.

Neoadjuvant PARPi or chemotherapy in ovarian cancer informs targeting effector Treg cells for homologous-recombination-deficient tumors.

Homologous recombination deficiency (HRD) is prevalent in cancer, sensitizing tumor cells to poly (ADP-ribose) polymerase (PARP) inhibition. However, the impact of HRD and related therapies on the tumor microenvironment (TME) remains elusive. Our study generates single-cell gene expression and T cell receptor profiles, along with validatory multimodal datasets from >100 high-grade serous ovarian cancer (HGSOC) samples, primarily from a phase II clinical trial (NCT04507841). Neoadjuvant monotherapy with the PARP inhibitor (PARPi) niraparib achieves impressive 62.5% and 73.6% response rates per RECIST v.1.1 and GCIG CA125, respectively. We identify effector regulatory T cells (eTregs) as key responders to HRD and neoadjuvant therapies, co-occurring with other tumor-reactive T cells, particularly terminally exhausted CD8+ T cells (Tex). TME-wide interferon signaling correlates with cancer cells upregulating MHC class II and co-inhibitory ligands, potentially driving Treg and Tex fates. Depleting eTregs in HRD mouse models, with or without PARP inhibition, significantly suppresses tumor growth without observable toxicities, underscoring the potential of eTreg-focused therapeutics for HGSOC and other HRD-related tumors.

Analysis of the effects of early screening combined with blood lactate on the severity of patients with sepsis.

This work aimed to investigate the adoption value of blood lactic acid (BLA) combined with the National Early Warning Score (NEWS) in the early screening of sepsis patients and assessing their severity. The data and materials utilized in this work were obtained from the electronic medical record system of 537 anonymized sepsis patients who received emergency rescue in the emergency rescue area of Liuzhou People's Hospital, Guangxi, from July 1, 2020, to December 26, 2020. Based on the 28-day outcomes of sepsis patients, the medical records were rolled into Group S (407 survival cases) and Group D (130 dead cases). Basic information such as the mode of hospital admission, initial management, use of emergency ventilator within 24 h of admission, NEWS score, arterial oxygen pressure/alveolar oxygen pressure ratio (PaO2/PAO2), alveolar-arterial oxygen difference (A-aDO2), serum creatinine (SCr), blood urea nitrogen (BUN), oxygenation index (OI), Glasgow Coma Scale (GCS), D-dimer, use of vasoactive drugs within 24 h of admission, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), quick Sequential Organ Failure Assessment (qSOFA) score, SOFA score, BLA level, NEWS with lactate (NEWS-L) score, SOFA score including lactate level (SOFA-L) score, Intensive Care Unit (ICU) length of stay, total hospital stay, ICU stay/total hospital stay, and septic shock condition were compared between groups. Logistic regression analysis was performed to assess the impact of various predictive factors on prognosis and to plot the receiver operating characteristic (ROC) curve. The results suggested marked differences between Group S and Group D in terms of mean age (t = -5.620; OR = -9.96, 95 % CI: -13.44∼-6.47; P < 0.001). Group S showed drastic differences in terms of mode of hospital admission (χ2 = 9.618, P < 0.01), method of initial management (χ2 = 51.766, P < 0.001), use of emergency ventilator within 24 h of admission (χ2 = 98.564, P < 0.001), incidence of septic shock (χ2 = 77.545, P < 0.001), use of vasoactive drugs within 24 h of admission (χ2 = 102.453, P < 0.001), heart rate (t = -4.063, P < 0.001), respiratory rate (t = -4.758, P < 0.001), oxygenation status (χ2 = 20.547, P < 0.001), NEWS score (t = -6.120, P < 0.001), PaO2/PAO2 ratio (t = 2.625, P < 0.01), A-aDO2 value (Z = -3.581, P < 0.001), OI value (Z = -3.106, P < 0.01), PLT value (Z = -2.305, P < 0.05), SCr value (Z = -3.510, P < 0.001), BUN value (Z = -3.170, P < 0.01), D-dimer (Z = -4.621, P < 0.001), CRP level (Z = -4.057, P < 0.001), PCT value (Z = -2.783, P < 0.01), IL-6 level (Z = -2.904, P < 0.001), length of hospital stay (Z = -4.138, P < 0.001), total hospital stay (Z = -8.488, P < 0.001), CCU/total hospital stay (Z = -9.118, P < 0.001), NEWS score (t = -6.120, P < 0.001), SOFA score (t = -6.961, P < 0.001), SOFA-L score (Z = -4.609, P < 0.001), NEWS-L score (Z = -5.845, P < 0.001), BLA level (Z = -6.557, P < 0.001), and GCS score (Z = 6.909, P < 0.001) when compared to Group D. The use of ventilators, septic shock, PCT, NEWS score, GCS score, SOFA score, SOFA-L score, NEWS-L score, and BLA level were identified as independent risk factors for predicting the prognosis of sepsis patients (P < 0.001). The areas under ROC curve (AUC) of blood lactic acid, PCT, NEWS, NEWS-L, GCS, SOFA, and SOFA-L were 0.695, 0.665, 0.692, 0.698, 0.477, 0.700, and 0.653, respectively. These findings indicate that the combination of BLA with NEWS (NEWS-L) score and SOFA score has certain advantages in assessing the prognosis of sepsis.

Cryptococcosis Associated With Biologic Therapy: A Narrative Review.

Cryptococcus is an opportunistic fungal pathogen that can cause disseminated infection with predominant central nervous system involvement in patients with compromised immunity. Biologics are increasingly used in the treatment of neoplasms and autoimmune/inflammatory conditions and the prevention of transplant rejection, which may affect human defense mechanisms against cryptococcosis. In this review, we comprehensively investigate the association between cryptococcosis and various biologics, highlighting their risks of infection, clinical manifestations, and clinical outcomes. Clinicians should remain vigilant for the risk of cryptococcosis in patients receiving biologics that affect the Th1/macrophage activation pathways, such as tumor necrosis factor α antagonists, Bruton tyrosine kinase inhibitors, fingolimod, JAK/STAT inhibitors (Janus kinase/signal transducer and activator of transcription), and monoclonal antibody against CD52. Other risk factors-such as age, underlying condition, and concurrent immunosuppressants, especially corticosteroids-should also be taken into account during risk stratification.

Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.

BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients. METHODS: In this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using the Res-Net 34 Multi-stage joint method. RESULTS: Of the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve (AUC) of 0.98 and a high QWK score of 0.94. CONCLUSIONS: In this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provided a reference for clinical application and pneumoconiosis screening.

Changes of crystalline structure and physicochemical properties of Pueraria lobata var. thomsonii starch under water deficit.

Crystal type is an important physicochemical property of starch. However, it is currently unclear whether changes in crystal type affect other properties of starch. This study discovered that water deficit resulted in an increase in small starch granules and transparency in Pueraria lobata var. thomsonii, while causing a decrease in amylose content and swelling power. Additionally, the crystal type of P. Thomsonii starch changed from CB-type to CA-type under water deficit, without significantly altering the short-range ordered structure and chain length distribution of starch. This transformation in crystal type led to peak splitting in the DSC heat flow curve of starch, alterations in gelatinization behavior, and an increase in resistant starch content. These changes in crystalline structure and physicochemical properties of starch granules are considered as adaptive strategies employed by P. Thomsonii to cope with water deficit.

CB2 stimulation of adipose resident ILC2s orchestrates immune balance and ameliorates type 2 diabetes mellitus.

Development of type 2 diabetes mellitus (T2DM) is associated with low-grade chronic type 2 inflammation and disturbance of glucose homeostasis. Group 2 innate lymphoid cells (ILC2s) play a critical role in maintaining adipose homeostasis via the production of type 2 cytokines. Here, we demonstrate that CB2, a G-protein-coupled receptor (GPCR) and member of the endocannabinoid system, is expressed on both visceral adipose tissue (VAT)-derived murine and human ILC2s. Moreover, we utilize a combination of ex vivo and in vivo approaches to explore the functional and therapeutic impacts of CB2 engagement on VAT ILC2s in a T2DM model. Our results show that CB2 stimulation of ILC2s protects against insulin-resistance onset, ameliorates glucose tolerance, and reverses established insulin resistance. Our mechanistic studies reveal that the therapeutic effects of CB2 are mediated through activation of the AKT, ERK1/2, and CREB pathways on ILC2s. The results reveal that the CB2 agonist can serve as a candidate for the prevention and treatment of T2DM.

Non-target discovery and risk prediction of per- and polyfluoroalkyl substances (PFAS) and transformation products in wastewater treatment systems.

Wastewater treatment plants (WWTPs) serve as the main destination of many wastes containing per- and polyfluoroalkyl substances (PFAS). Here, we investigated the occurrence and transformation of PFAS and their transformation products (TPs) in wastewater treatment systems using high-resolution mass spectrometry-based target, suspect, and non-target screening approaches. The results revealed the presence of 896 PFAS and TPs in aqueous and sludge phases, of which 687 were assigned confidence levels 1-3 (46 PFAS and 641 TPs). Cyp450 metabolism and environmental microbial degradation were found to be the primary metabolic transformation pathways for PFAS within WWTPs. An estimated 52.3 %, 89.5 %, and 13.6 % of TPs were believed to exhibit persistence, bioaccumulation, and toxicity effects, respectively, with a substantial number of TPs posing potential health risks. Notably, the length of the fluorinated carbon chain in PFAS and TPs was likely associated with increased hazard, primarily due to the influence of biodegradability. Ultimately, two high riskcompounds were identified in the effluent, including one PFAS (Perfluorobutane sulfonic acid) and one enzymatically metabolized TP (23-(Perfluorobutyl)tricosanoic acid@BTM0024_cyp450). It is noteworthy that the toxicity of some TPs exceeded that of their parent compounds. The results from this study underscores the importance of PFAS TPs and associated environmental risks.

Impaired GPX4 activity elicits ferroptosis in alveolar type II cells promoting PHMG-induced pulmonary fibrosis development.

Inhaling polyhexamethylene guanidine (PHMG) aerosol, a broad-spectrum disinfectant, can lead to severe pulmonary fibrosis. Ferroptosis, a form of programmed cell death triggered by iron-dependent lipid peroxidation, is believed to play a role in the chemical-induced pulmonary injury. This study aimed to investigate the mechanism of ferroptosis in the progression of PHMG-induced pulmonary fibrosis. C57BL/6 J mice and the alveolar type II cell line MLE-12 were used to evaluate the toxicity of PHMG in vivo and in vitro, respectively. The findings indicated that iron deposition was observed in PHMG induced pulmonary fibrosis mouse model and ferroptosis related genes have changed after 8 weeks PHMG exposure. Additionally, there were disturbances in the antioxidant system and mitochondrial damage in MLE-12 cells following a 12-hour treatment with PHMG. Furthermore, the study observed an increase in lipid peroxidation and a decrease in GPX4 activity in MLE-12 cells after exposure to PHMG. Moreover, pretreatment with the ferroptosis inhibitors Ferrostatin-1 (Fer-1) and Liproxstatin-1 (Lip-1) not only restored the antioxidant system and GPX4 activity but also mitigated lipid peroxidation. Current data exhibit the role of ferroptosis pathway in PHMG-induced pulmonary fibrosis and provide a potential target for future treatment.

Integrated multi-omic analysis reveals the carbon metabolism-mediated regulation of polysaccharide biosynthesis by suitable light intensity in Bletilla striata leaves.

Bletilla striata, valued for its medicinal and ornamental properties, remains largely unexplored in terms of how light intensity affects its physiology, biochemistry, and polysaccharide formation. In this 5-month study, B. striata plants were exposed to three different light intensities: low light (LL) (5-20 µmol m-2·s-1), middle light (ML) (200 µmol m-2·s-1), and high light (HL) (400 µmol m-2·s-1). The comprehensive assessment included growth, photosynthetic apparatus, chlorophyll fluorescence electron transport, and analysis of differential metabolites based on the transcriptome and metabolome data. The results indicated that ML resulted in the highest plant height and total polysaccharide content, enhanced photosynthetic apparatus performance and light energy utilization, and stimulated carbon metabolism and carbohydrate accumulation. HL reduced Chl content and photosynthetic apparatus functionality, disrupted OEC activity and electron transfer, stimulated carbon metabolism and starch and glucose accumulation, and hindered energy metabolism related to carbohydrate degradation and oxidation. In contrast, LL facilitated leaf growth and increased chlorophyll content but decreased plant height and total polysaccharide content, compromised the photosynthetic apparatus, hampered light energy utilization, stimulated energy metabolism related to carbohydrate degradation and oxidation, and inhibited carbon metabolism and carbohydrate synthesis. Numerous genes in carbon metabolism were strongly related to polysaccharide metabolites. The katE and cysK genes in carbon metabolism were strongly related not only to polysaccharide metabolites, but also to genes involved in polysaccharide biosynthesis. Our results highlight that light intensity plays a crucial role in affecting polysaccharide biosynthesis in B. striata, with carbon metabolism acting as a mediator under suitable light intensity conditions.

Effect of psyllium husk powder on the gelation behavior, microstructure, and intermolecular interactions in myofibrillar protein gels from Andrias davidianus.

The interaction between proteins and soluble dietary fibers plays a vital role in the development of animal-derived foods. Herein, the effects of different contents (0-3.0%) of round-bracted psyllium husk powder (PHP) on the gelation behavior, microstructure, and intermolecular interactions of Andrias davidianus myofibrillar protein (MP) were investigated. Rheological and chemical forces suggested that PHP (1.5%-2.0%) enhanced the functional properties of MP at low ionic strength, thereby increasing the viscoelasticity of mixed gels. SDS-PAGE revealed that PHP reinforced the cross-linking and aggregation of protein molecules. Circular dichroism spectroscopy, low-field nuclear magnetic resonance, and scanning electron microscopy demonstrated that PHP induced the transformation of α-helix (decreased by 14.85%) to an ordered ß-sheet structure (increased by 81.58%), which was more favorable for the formation of dense network structure and improved (10.53%) the water retention of MP gels. This study provided new insights for PHP to effectively meliorate the heat-induced gelling properties of MP.

Incidence rate and risk factors for suicide in patients with breast cancer in the USA: A surveillance, epidemiology, and end results analysis (SEER).

PURPOSE: To investigate suicide mortality and the related factors among female breast cancer patients in the United States. METHODS: The SEER database was used to identify 716,422 patients diagnosed with breast cancer between 2010 and 2018 to calculate a standardized mortality rate (SMR). An analysis of risk factors for suicide death was conducted using the univariate and multivariate Cox proportional risk model. An estimation of suicide probability was performed through a nomogram model. RESULTS: Compared with the expected suicide cases (n = 155) in the general population of the United States at the corresponding period (a suicide death rate of 5.71 per 100,000 person-years), the suicide rate among 716,422 breast cancer patients was followed during 2010-2018 and showed a relatively higher rate of 9.02 per 100,000 person-years. The SMR was 1.58 (95%CI: 1.39-1.79). White and other races were nine and seven times more likely to complete suicide than Black race, respectively (aHR = 9.013, 95%CI: 3.335-24.36, P < 0.001; aHR = 7.129, 95%CI: 2.317-21.931, P = 0.001); unmarried or single patients were at higher risk than married patients (aHR = 1.693, 95%CI: 1.206-2.377, P = 0.002). Patients receiving radiotherapy (aHR = 0.731, 95%CI: 0.545-0.980, P = 0.036) were less likely to complete suicide than those who did not. CONCLUSION: Female breast cancer patients in the United States have a higher suicide rate than the general public, and the risk factors consist of non-black ethnicity, being single or unmarried, and not being treated with radiotherapy. As a result of this study, clinicians may be able to identify female breast cancer patients who are at high risk of suicide, thus providing appropriate psychological support at the early stage.

Selective and quasi-continuous switching of ferroelectric Chern insulator devices for neuromorphic computing.

Quantum materials exhibit dissipationless topological edge state transport with quantized Hall conductance, offering notable potential for fault-tolerant computing technologies. However, the development of topological edge state-based computing devices remains a challenge. Here we report the selective and quasi-continuous ferroelectric switching of topological Chern insulator devices, showcasing a proof-of-concept demonstration in noise-immune neuromorphic computing. We fabricate this ferroelectric Chern insulator device by encapsulating magic-angle twisted bilayer graphene with doubly aligned h-BN layers and observe the coexistence of the interfacial ferroelectricity and the topological Chern insulating states. The observed ferroelectricity exhibits an anisotropic dependence on the in-plane magnetic field. By tuning the amplitude of the gate voltage pulses, we achieve ferroelectric switching between any pair of Chern insulating states in the presence of a finite magnetic field, resulting in 1,280 ferroelectric states with distinguishable Hall resistance levels on a single device. Furthermore, we demonstrate deterministic switching between two arbitrary levels among the record-high number of ferroelectric states. This unique switching capability enables the implementation of a convolutional neural network resistant to external noise, utilizing the quantized Hall conductance levels of the Chern insulator device as weights. Our study provides a promising avenue towards the development of topological quantum neuromorphic computing, where functionality and performance can be drastically enhanced by topological quantum materials.

Design of NiCoP nanorod loaded on cocoon carbon substrate and its non-metal doping for efficient hydrogen evolution.

The quest for effective and sustainable electrocatalysts for hydrogen evolution is crucial in advancing the widespread use of H2. In this study, we utilized silkworm cocoons as the source material to produce porous N-doped carbon (PNCC) substrates through a process involving degumming and annealing. Subsequently, NiCoP nanorod (NiCoP@PNCC) is deposited onto the substrates via a simple impregnation and calcination method to enhance the catalytic performance for the hydrogen evolution reaction (HER). The optimal spacing between the silk fibers of PNCC facilitates longitudinal growth, increases the active surface area, and balances the adsorption and desorption of reaction intermediates, thereby accelerating HER kinetics. Consequently, NiCoP@PNCC demonstrates impressive performance, with 44 mV overpotential to achieve a current density of 10 mA cm-2. Additionally, density functional theory (DFT) calculations reveal that the electronic structure and energy band of NiCoP@PNCC can be modified through the doping of elements such as B, C, N, O, F, and S. In addition, with the electronegativity enhancement of the doping elements, the interaction between Co atoms in NiCoP@PNCC and O atoms in adsorbed H2O molecules gradually enhanced, which is conducive to the dissociation of water in alkaline solution. This research introduces a novel approach for fine-tuning the catalytic activity of transition metal phosphides.

Clinical Significance of Long Non-Coding RNA SNHG5 in the Diagnosis and Prognosis of Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is preventable and requires early screening. The study aimed to examine the clinical values of long non-coding RNA (lncRNA) SNHG5 in COPD diagnosis and prognosis. Out of 160 COPD patients, 80 were in the stable stage and 80 were in the acute exacerbation of COPD stage (AECOPD). SNHG5 expression was detected via qRT-PCR. The survival analysis was conducted using Cox regression analysis and K-M curve. SNHG5 levels significantly reduced in both stable COPD and AECOPD groups compared with the control group, with AECOPD group recording the lowest values. SNHG5 levels were negatively correlated with GOLD stage. Serum SNHG5 can differentiate stable COPD patients from healthy individuals (AUC = 0.805), and can screen AECOPD from stable ones (AUC = 0.910). SNHG5 negatively influenced the release of inflammatory cytokines. For AECOPD patients, those with severe cough and wheezing dyspnea symptoms exhibited the lowest values of SNUG5. Among the 80 AECOPD patients, 16 cases died in the one-year follow-up, all of whom had low levels of SNHG5. SNHG5 levels independently influenced survival outcomes, patients with low SNHG5 levels had a poor prognosis. Thus, lncRNA SNHG5, which is downregulated in patients with COPD (especially AECOPD), can potentially protect against AECOPD and serve as a novel prognostic biomarker for AECOPD.