Establishing a course to train ICU nurses on prone position mechanical ventilation: A Delphi study.

AIM: To develop a comprehensive training course for training ICU nurses in prone positioning. DESIGN: A mixed study combining semi-structured interviews and two rounds of Delphi surveys. METHODS: We constructed a questionnaire after collecting data through a literature review and semi-structured interviews. We used the Delphi expert correspondence method to conduct two rounds of research among 17 experts in the field of critical illness. Data collection took place between May and August 2022. RESULTS: The effective questionnaire recovery rate was 88.2%. The expert authority coefficient was 0.876; the Kendall coordination coefficient was 0.402; the average importance score for each index ranged from 4.00 to 4.93; and the coefficient of variation for each index ranged from 0.05 to 0.19. We established 13 second-level indicators and 41 third-level indicators on prone position ventilation training according to three aspects: training contents, training methods and training assessment. The training system of prone mechanical ventilation for ICU nurses established in this study will provide an effective framework for training and evaluating the practical ability of prone mechanical ventilation for ICU nurses.

The association between HLA-B variants and amoxicillin-induced severe cutaneous adverse reactions in Chinese han population.

Background: Amoxicillin (AMX) is among the most prescribed and the best tolerated antimicrobials worldwide. However, it can occasionally trigger severe cutaneous adverse reactions (SCAR) with a significant morbidity and mortality. The genetic factors that may be relevant to AMX-induced SCAR (AMX-SCAR) remain unclear. Identification of the genetic risk factor may prevent patients from the risk of AMX exposure and resume therapy with other falsely implicated drugs. Methodology: Four patients with AMX-SCAR, 1,000 population control and 100 AMX-tolerant individuals were enrolled in this study. Both exome-wide and HLA-based association studies were conducted. Molecular docking analysis was employed to simulate the interactions between AMX and risk HLA proteins. Results: Compared with AMX-tolerant controls, a significant association of HLA-B*15:01 with AMX-SCAR was validated [odds ratio (OR) = 22.9, 95% confidence interval (CI): 1.68-1275.67; p = 7.34 × 10-3]. Moreover, 75% carriers of HLA-B*15:01 in four patients with AMX-SCAR, and the carrier frequency of 10.7% in 1,000 control individuals and 11.0% in 100 AMX-tolerant controls, respectively. Within HLA-B protein, the S140 present in all cases and demonstrated the strongest association with AMX-SCAR [OR = 53.5, p = 5.18 × 10-4]. Molecular docking results also confirmed the interaction between AMX and S140 of the HLA-B protein, thus eliminating the false-positive results during in association analysis. Conclusion: Our findings suggest that genetic susceptibility may be involved in the development of AMX-SCAR in Han Chinese. However, whether the HLA-B variants observed in this study can be used as an effective genetic marker of AMX-induced SCAR still needs to be further explored in larger cohort studies and other ethnic populations.

Development and Validation of Nomograms for Predicting Pneumonia in Patients with COVID-19 and Lung Cancer.

Background: COVID-19 has spread worldwide, becoming a global threat to public health and can lead to complications, especially pneumonia, which can be life-threatening. However, in lung cancer patients, the prediction of pneumonia and severe pneumonia has not been studied. We aimed to develop effective models to assess pneumonia after SARS-CoV-2 infection in lung cancer patients to guide COVID-19 management. Methods: We retrospectively recruited 621 lung cancer patients diagnosed with COVID-19 via SARS-CoV-2 RT-PCR analysis in two medical centers and divided into training and validation group, respectively. Univariate and multivariate logistic regression analysis were used to identify independent risk factors of all-grade pneumonia and ≥ grade 2 pneumonia in the training group. Nomograms were established based on independent predictors and verified in the validation group. C-index, ROC curves, calibration curve, and DCA were used to evaluate the nomograms. Subgroup analyses in immunotherapy or thoracic radiotherapy patients were then conducted. Results: Among 621 lung cancer patients infected with SARS-CoV-2, 203 (32.7%) developed pneumonia, and 66 (10.6%) were ≥ grade 2. Multivariate logistic regression analysis showed that diabetes, thoracic radiotherapy, low platelet and low albumin at diagnosis of COVID-19 were significantly associated with all-grade pneumonia. The C-indices of the prediction nomograms in the training group and validation group were 0.702 and 0.673, respectively. Independent predictors of ≥ grade 2 pneumonia were age, KPS, thoracic radiotherapy, platelet and albumin at COVID 19 diagnosis, with C-indices of 0.811 and 0.799 in the training and validation groups. In the thoracic radiotherapy subgroup, 40.8% and 11% patients developed all-grade and ≥grade 2 pneumonia, respectively. The rates in the immunotherapy subgroup were 31.3% and 6.6%, respectively. Conclusion: We developed nomograms predicting the probability of pneumonia in lung cancer patients infected with SARS-CoV-2. The models showed good performance and can be used in the clinical management of COVID-19 in lung cancer patients. Higher-risk patients should be managed with enhanced protective measures and appropriate intervention.

Seasonal and Spatial Distribution Characteristics of Sepia esculenta in the East China Sea Region: Transfer of the Central Distribution from 29° N to 28° N.

The golden cuttlefish (Sepia esculenta) is an important cephalopod species with a lifespan of approximately one year. The species plays a crucial role in marine ecological support services and is commercially valuable in fisheries. In the seas around China, this species has emerged as the main target for cuttlefish fisheries, replacing Sepiella maindroni since the 1990s. Variations in oceanographic conditions associated with global warming could significantly impact the temporal-spatial distribution of the species. In this study, we performed bottom trawling surveys with four cruises during 2018-2019 in the East China Sea region to determine the current resource status and seasonal-spatial variations in S. esculenta. We found that the average individual weight (AIW) values were 4.87 and 519.00 g/ind at stations located at 30.50° N, 124.00° E and 30.50° N, 124.50° E, respectively, with the aggregation of larvae and parent groups in spring. The species was not distributed north of 32.00° N in summer. The catch per unit effort by weight (CPUEw) value decreased in the order of 2772.50→2575.20→503.29→124.36 g/h, corresponding to latitudes of 34.50° N→34.00° N→33.50° N→32.50° N 121.50° E in autumn. The most suitable fishing areas were the south of the East China Sea region in spring; the south of the East China Sea region extending to the center and outer parts of the East China Sea region in summer; the south of the Yellow Sea close to the Haizhou Bay fishing ground and the forbidden fishing line region of the Lusi and Dasha fishing grounds in autumn; and the south and center of the East China Sea region in winter. The most suitable sea bottom temperature (SBT) values from spring to winter were 14.76-20.53 °C, 19.54-22.98 °C, 11.79-17.64 °C, and 16.94-20.36 °C, respectively. The most suitable sea bottom salinity (SBS) values were 31.53-34.80‱ in spring, 32.95-34.68‱ in summer, 31.51-34.77‱ in autumn, and 33.82-34.51‱ in winter. We concluded the following: (1) the southern and northern areas of the East China Sea region are spawning and nursery grounds, respectively, in spring; (2) the central distribution is located at a latitude of 28.00° N in autumn and winter; and (3) the southern area of the Yangtze River to the north is a spawning ground in spring, and the areas located at 29.00-34.50° N, 124.00-124.50° E, and 28.00-30.50° N, 125.50-126.50° E are nursery grounds. The results of this study provide useful guidance for appropriate fisheries management, thereby avoiding a collapse in the S. esculenta population, which has been experienced in other species in this area.

Enhanced Oxygen Evolution and Zinc-Air Battery Performance via Electronic Spin Modulation in Heterostructured Catalysts.

Beyond optimizing electronic energy levels, the modulation of the electronic spin configuration is an effective strategy, often overlooked, to boost activity and selectivity in a range of catalytic reactions, including the oxygen evolution reaction (OER). This electronic spin modulation is frequently accomplished using external magnetic fields, which makes it impractical for real applications. Herein, spin modulation is achieved by engineering Ni/MnFe2O4 heterojunctions, whose surface is reconstructed into NiOOH/MnFeOOH during the OER. NiOOH/MnFeOOH shows a high spin state of Ni, which regulates the OH- and O2 adsorption energy and enables spin alignment of oxygen intermediates. As a result, NiOOH/MnFeOOH electrocatalysts provide excellent OER performance with an overpotential of 261 mV at 10 mA cm-2. Besides, rechargeable zinc-air batteries based on Ni/MnFe2O4 show a high open circuit potential of 1.56 V and excellent stability for more than 1000 cycles. This outstanding performance is rationalized using density functional theory calculations, which show that the optimal spin state of both Ni active sites and oxygen intermediates facilitates spin-selected charge transport, optimizes the reaction kinetics, and decreases the energy barrier to the evolution of oxygen. This study provides valuable insight into spin polarization modulation by heterojunctions enabling the design of next-generation OER catalysts with boosted performance.

How is work-family conflict linked to nurse-assessed patient safety among intensive care unit nurses? A serial multiple mediation analysis.

AIM: The aim of this study was to test whether rumination and negative affectivity mediate the relationship between work-family conflict and nurse-assessed patient safety among intensive care unit nurses. BACKGROUND: Most intensive care unit nurses experience work-family conflicts that jeopardise patient safety. Although prior studies have explored the effect of work-family conflict on patient safety, few have investigated whether work-family conflict is associated with patient safety through rumination and negative affectivity among intensive care unit nurses. DESIGN: Cross-sectional study. METHODS: This study included 209 intensive care unit nurses from five general hospitals. The Work-Family Conflict Scale, the Ruminative Response Scale, the Positive and Negative Affect Schedule-Negative Affectivity, and three items indicating nurses' perception of overall patient safety were used to gather data. Associations between work-family conflict, rumination, negative affectivity, and nurse-assessed patient safety were assessed using correlation and serial multiple mediation analysis. RESULTS: Work-family conflict, rumination, negative affectivity, and nurse-assessed patient safety were significantly correlated (p < 0.01). Work-family conflict can have not only a direct negative impact on the nurse-assessed patient safety (effect = -0.0234; standard error [SE] = 0.0116; 95% confidence interval [CI]: lower limit [LL] = -0.0464, upper limit [UL] = -0.0005) but also an indirect impact on nurse-assessed patient safety through three paths: the independent mediating role of rumination (effect = -0.0118; SE = 0.0063; 95% CI: LL = -0.0251, UL = -0.0006), the independent mediating role of negative affectivity (effect = -0.0055; SE = 0.0039; 95% CI: LL = -0.0153, UL = -0.0001), and the chain-mediating role of rumination and negative affectivity (effect = -0.0078; SE = 0.0031; 95% CI: LL = -0.0152, UL = -0.0027). CONCLUSION: Our findings indicated that work-family conflict could influence nurse-assessed patient safety through increasing rumination and negative affectivity among intensive care unit nurses. Based on the results, interventions aimed at decreasing work-family conflict would be beneficial for intensive care unit nurses' emotional stability and patient safety.

Golgi-targeted NIR fluorescent probe with large stokes shift for real-time monitoring of nitric oxide in depression model.

Depression is a debilitating mental illness that poses a serious threat to human health. Nitric Oxide (NO), as an important gasotransmitter, is closely associated with the pathogenesis of depressive disorders. Effective monitoring of NO fluctuation is beneficial for the diagnosis of depression and therapy assessment of antidepressants. Currently, there is a lack of effective methods for rapidly and sensitively identifying NO and elucidating its relationship with depression diseases. Herein, we developed a NIR dye TJ730-based fluorescent probe TJ730-Golgi-NO incorporating benzenesulfonamide as a Golgi-targeted moiety and the thiosemicarbazide group for NO detection. The probe exhibited turn-on fluorescence ability and a large Stokes shift of 158 nm, which shows high sensitivity, selectivity, and rapid response (<1 min) for NO detection. TJ730-Golgi-NO could detect exogenous and endogenous NO in cells stimulated by Glu and LPS, and target Golgi apparatus. Moreover, we disclose a significant increase of NO in the depression model and a weak fluorescence evidenced in the fluoxetine-treated depression mice. This study provides a competent tool for studying the function of NO and helping improve the effective treatment of depression diseases.

A spatially localized DNA linear classifier for cancer diagnosis.

Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more efficient computing scheme, higher modularity with scaled-up circuity and stronger tolerance of corrupted inputs in a complex environment. Towards these goals, we construct a spatially localized, DNA integrated circuits-based classifier (DNA IC-CLA) that can perform neuromorphic architecture-based computation at a molecular level for medical diagnosis. The DNA-based classifier employs a two-dimensional DNA origami as the framework and localized processing modules as the in-frame computing core to execute arithmetic operations (e.g. multiplication, addition, subtraction) for efficient linear classification of complex patterns of miRNA inputs. We demonstrate that the DNA IC-CLA enables accurate cancer diagnosis in a faster (about 3 h) and more effective manner in synthetic and clinical samples compared to those of the traditional freely diffusible DNA circuits. We believe that this all-in-one DNA-based classifier can exhibit more applications in biocomputing in cells and medical diagnostics.

Comparison of inflammatory markers, coagulation indicators and outcomes between influenza and COVID-19 infection amongst children: A systematic review and meta-analysis.

Background: Influenza and COVID-19 patients share similar features and outcomes amongst adults. However, the difference between these diseases is not explored in paediatric age group especially in terms of inflammatory markers, coagulation profile and outcomes. Hence, we did this review to compare the inflammatory, coagulation features and outcomes between influenza and COVID-19 infected children. Methods: Literature search was done in PubMed Central, Scopus, EMBASE, CINAHL, Cochrane library, Google Scholar & ScienceDirect from November 2019 to May 2022. Risk of bias assessment was done through Newcastle Ottawa scale. Meta-analysis was done using random-effects model and the final pooled estimate was reported as pooled odds ratio (OR) or standardized mean difference (SMD) along with 95 % confidence interval (CI) depending on the type of outcome. Results: About 16 studies were included with most studies having higher risk of bias. Influenza paediatric patients had significantly higher erythrocyte sedimentation rate (ESR) (pooled SMD = 0.60; 95%CI: 0.30-0.91; I2 = 0 %), lactate dehydrogenase (LDH) (pooled SMD = 2.01; 95%CI: 0.37-3.66; I2 = 98.4 %) and prothrombin time (PT) (pooled SMD = 2.12; 95%CI: 0.44-3.80; I2 = 98.3 %) when compared to paediatric COVID-19 patients. There was no significant difference in terms of features like CRP, procalcitonin, serum albumin, aPTT, mortality and need for mechanical ventilation. Conclusion: Inflammatory markers like ESR, LDH and PT was significantly higher in influenza patients when compared to COVID-19 in children, while rest of the markers and adverse clinical outcomes were similar between both the groups. Identification of these biomarkers has helped in understanding the distinctness of COVID-19 and influenza virus and develop better management strategies.

Recent progress and outlooks in rhodamine-based fluorescent probes for detection and imaging of reactive oxygen, nitrogen, and sulfur species.

Reactive oxygen species (ROS), reactive nitrogen species (RNS), and reactive sulfur species (RSS) serve as vital mediators essential for preserving intracellular redox homeostasis within the human body, thereby possessing significant implications across physiological and pathological domains. Nevertheless, deviations from normal levels of ROS, RNS, and RSS disturb redox homeostasis, leading to detrimental consequences that compromise bodily integrity. This disruption is closely linked to the onset of various human diseases, thereby posing a substantial threat to human health and survival. Small-molecule fluorescent probes exhibit considerable potential as analytical instruments for the monitoring of ROS, RNS, and RSS due to their exceptional sensitivity and selectivity, operational simplicity, non-invasiveness, localization capabilities, and ability to facilitate in situ optical signal generation for real-time dynamic analyte monitoring. Due to their distinctive transition from their spirocyclic form (non-fluorescent) to their ring-opened form (fluorescent), along with their exceptional light stability, broad wavelength range, high fluorescence quantum yield, and high extinction coefficient, rhodamine fluorophores have been extensively employed in the development of fluorescent probes. This review primarily concentrates on the investigation of fluorescent probes utilizing rhodamine dyes for ROS, RNS, and RSS detection from the perspective of different response groups since 2016. The scope of this review encompasses the design of probe structures, elucidation of response mechanisms, and exploration of biological applications.

RAS-RAF-miR-296-3p signaling axis increases Rad18 expression to augment radioresistance in pancreatic and thyroid cancers.

Oncogenic RAS and RAF signaling has been implicated in contributing to radioresistance in pancreatic and thyroid cancers. In this study, we sought to better clarify molecular mechanisms contributing to this effect. We discovered that miRNA 296-3p (miR-296-3p) is significantly correlated with radiosensitivity in a panel of pancreatic cancer cells, and miR-296-3p is highly expressed in normal cells, but low in cancer cell lines. Elevated expression of miR-296-3p increases radiosensitization while decreasing the expression of the DNA repair enzyme RAD18 in both pancreatic and thyroid cancer cells. RAD18 is overexpressed in both pancreatic and thyroid tumors compared to matched normal controls, and high expression of RAD18 in tumors is associated with poor prognostic features. Modulating the expression of mutant KRAS in pancreatic cancer cells or mutant BRAF in thyroid cancer cells demonstrates a tight regulation of RAD18 expression in both cancer types. Depletion of RAD18 results in DNA damage and radiation-induced cell death. Importantly, RAD18 depletion in combination with radiotherapy results in marked and sustained tumor regression in KRAS mutant pancreatic cancer orthotopic tumors and BRAF mutant thyroid heterotopic tumors. Overall, our findings identify a novel coordinated RAS/RAF-miR-296-3p-RAD18 signaling network in pancreatic and thyroid cancer cells, which leads to enhanced radioresistance.

Aging-related biomarker discovery in the era of immune checkpoint inhibitors for cancer patients.

Older patients with cancer, particularly those over 75 years of age, often experience poorer clinical outcomes compared to younger patients. This can be attributed to age-related comorbidities, weakened immune function, and reduced tolerance to treatment-related adverse effects. In the immune checkpoint inhibitors (ICI) era, age has emerged as an influential factor impacting the discovery of predictive biomarkers for ICI treatment. These age-linked changes in the immune system can influence the composition and functionality of tumor-infiltrating immune cells (TIICs) that play a crucial role in the cancer response. Older patients may have lower levels of TIICs infiltration due to age-related immune senescence particularly T cell function, which can limit the effectivity of cancer immunotherapies. Furthermore, age-related immune dysregulation increases the exhaustion of immune cells, characterized by the dysregulation of ICI-related biomarkers and a dampened response to ICI. Our review aims to provide a comprehensive understanding of the mechanisms that contribute to the impact of age on ICI-related biomarkers and ICI response. Understanding these mechanisms will facilitate the development of treatment approaches tailored to elderly individuals with cancer.

The Potential Mediating Effect of Symptom Burden on Demoralization Through Locus of Control and Coping Strategies in Chinese Patients With Cancer.

BACKGROUND: Demoralization is a psychological syndrome that is highly prevalent in patients with cancer and detrimental to individuals' physical and mental health. To explore effective intervention, we first determined the relationships between locus of control, coping strategies, symptom burden, and demoralization. OBJECTIVE: The aim of this study was to determine the relationship between symptom burden, locus of control, coping strategies, and demoralization in patients with cancer. METHODS: In this descriptive-correlational study, 273 valid patients were selected with convenience sampling method from a hospital in China. Data were collected using the Chinese version of the M.D. Anderson Symptom Inventory, the Chinese version of the Multidimensional Health Locus of Control Scale, the Chinese version of the Medical Coping Modes Questionnaire, and the Mandarin version of the Demoralization Scale. Data were analyzed using descriptive and inferential statistics using SPSS and AMOS. RESULTS: A total of 115 patients (42.12%) experienced clinical demoralization (Mandarin version of the Demoralization Scale > 30). Symptom burden (ß = 0.295, P < .001), confrontation (ß = -0.117, P = .028), and resignation (ß = 0.456, P < .001) had direct effects on demoralization. Symptom burden also had an indirect effect on demoralization through the mediating role of resignation (ß = 0.026, P = .002). Meanwhile, locus of control can affect demoralization entirely through the indirect mediating role of coping strategies (chance locus of control via resignation [ß = 0.138, P < .01], powerful locus of control via confrontation [ß = -0.017, P < .05]). CONCLUSIONS: Symptom burden affects demoralization not only directly but also indirectly. Coping strategies play an important mediating role between symptom burden, locus of control, and demoralization in patients with cancer. IMPLICATIONS FOR PRACTICE: It is urgent to screen demoralization and identify patients with high symptom burden, maladaptive locus of control, or coping strategies. For the patients targeted, a more comprehensive and systematic approach to symptom management and more appropriate guidance related to adaptive coping strategies are needed.

A DNA circuit that records molecular events.

Characterizing the relative onset time, strength, and duration of molecular signals is critical for understanding the operation of signal transduction and genetic regulatory networks. However, detecting multiple such molecules as they are produced and then quickly consumed is challenging. A MER can encode information about transient molecular events as stable DNA sequences and are amenable to downstream sequencing or other analysis. Here, we report the development of a de novo molecular event recorder that processes information using a strand displacement reaction network and encodes the information using the primer exchange reaction, which can be decoded and quantified by DNA sequencing. The event recorder was able to classify the order at which different molecular signals appeared in time with 88% accuracy, the concentrations with 100% accuracy, and the duration with 75% accuracy. This simultaneous and highly programmable multiparameter recording could enable the large-scale deciphering of molecular events such as within dynamic reaction environments, living cells, or tissues.

Multi-omics analyses reveal interactions between the skin microbiota and skin metabolites in atopic dermatitis.

Introduction: Atopic dermatitis (AD) is one of the most common inflammatory skin diseases. Skin microecological imbalance is an important factor in the pathogenesis of AD, but the underlying mechanism of its interaction with humans remains unclear. Methods: 16S rRNA gene sequencing was conducted to reveal the skin microbiota dynamics. Changes in skin metabolites were tracked by LC-MS metabolomics. We then explored the potential mechanism of interaction by analyzing the correlation between skin bacterial communities and metabolites in corresponding skin-associated samples. Results: Samples from 18 AD patients and 18 healthy volunteers (HVs) were subjected to 16S rRNA gene sequencing and LC-MS metabolomics. AD patients had dysbiosis of the skin bacterial community with decreased species richness and evenness. The relative abundance of the genus Staphylococcus increased significantly in AD, while the abundances of the genera Propionibacterium and Brevundimonas decreased significantly. The relative abundance of the genera Staphylococcus in healthy females was significantly higher than those in healthy males, while it showed no difference in AD patients with or without lesions. The effects of AD status, sex and the presence or absence of rashes on the number of differentially abundant metabolites per capita were successively reduced. Multiple metabolites involved in purine metabolism and phenylalanine metabolism pathways (such as xanthosine/xanthine and L-phenylalanine/trans-cinnamate) were increased in AD patients. These trends were much more obvious between female AD patients and female HVs. Spearman correlation analysis revealed that the genus Staphylococcus was positively correlated with various compounds involved in phenylalanine metabolism and purine metabolic pathways. The genera Brevundimonas and Lactobacillus were negatively correlated with various compounds involved in purine metabolism, phenylalanine metabolism and sphingolipid signaling pathways. Discussion: We suggest that purine metabolism and phenylalanine metabolism pathway disorders may play a certain role in the pathogenic mechanism of Staphylococcus aureus in AD. We also found that females are more likely to be colonized by the genus Staphylococcus than males. Differentially abundant metabolites involved in purine metabolism and phenylalanine metabolism pathways were more obvious in female. However, we should notice that the metabolites we detected do not necessarily derived from microbes, they may also origin from the host.

Emodin-8-O-ß-D-glucopyranoside-induced hepatotoxicity and gender differences in zebrafish as revealed by integration of metabolomics and transcriptomics.

BACKGROUND: Emodin-8-O-ß-D-glucopyranoside (Em8G) is an active ingredient of traditional Chinese medicine Rhei Radix et Rhizoma and Polygonum multiflorum Thunb.. And it caused hepatotoxicity, while the underlying mechanism was not clear yet. PURPOSE: We aimed to explore the detrimental effects of Em8G on the zebrafish liver through the metabolome and transcriptome integrated analysis. STUDY DESIGN AND METHODS: In this study, zebrafish larvae were used in acute toxicity tests to reveal the hepatotoxicity of Em8G. Adult zebrafish were then used to evaluate the gender differences in hepatotoxicity induced by Em8G. Integration of transcriptomic and metabolomic analysis was used further to explore the molecular mechanisms underlying gender differences in hepatotoxicity. RESULTS: Our results showed that under non-lethal concentration exposure conditions, hepatotoxicity was observed in Em8G-treated zebrafish larvae, including changes in liver transmittance, liver area, hepatocyte apoptosis and hepatocyte vacuolation. Male adult zebrafish displayed a higher Em8G-induced hepatotoxicity than female zebrafish, as demonstrated by the higher mortality and histopathological alterations. The results of transcriptomics combined with metabolomics showed that Em8G mainly affected carbohydrate metabolism (such as TCA cycle) in male zebrafish and amino acid metabolism (such as arginine and proline metabolism) in females, suggesting that the difference of energy metabolism disorder may be the potential mechanism of male and female liver toxicity induced by Em8G. CONCLUSIONS: This study provided the direct evidence for the hepatotoxicity of Em8G to zebrafish models in vivo, and brought a new insight into the molecular mechanisms of Em8G hepatotoxicity, which can guide the rational application of this phytotoxin. In addition, our findings revealed gender differences in the hepatotoxicity of Em8G to zebrafish, which is related to energy metabolism and provided a methodological reference for evaluating hepatotoxic drugs with gender differences.

East Asian patients who received immunotherapy-based therapy associated with improved survival benefit in advanced non-small cell lung cancer: An updated meta-analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) combined with chemotherapy have been recommended as the standard treatment for advanced NSCLC patients without driver-gene mutations. However, there are different genetic characteristics and biological traits of tumors between non-East Asian (nEA) and East Asian (EA) patients with NSCLC, which may contribute to differences in the efficacy of ICIs in different ethnic populations. Previous findings regarding differences in the efficacy of ICIs among ethnic groups have been inconsistent. Therefore, we performed a meta-analysis by collecting published data to investigate the clinical outcomes of ICIs for EA NSCLC patients compared to nEA patients. METHODS: Overall survival (OS) and progression-free survival (PFS) were used to access the difference in survival outcomes between the two populations. Subgroup analyses were performed based on the line of ICIs, the use of ICIs alone or in combination, and the type of ICIs. RESULTS: A total of 9826 NSCLC patients from 21 randomized controlled trials (RCTs) with 4064 EAs were included, which involved PD-1, PD-L1, and CTLA-4 inhibitors. EA NSCLC patients who received ICIs-based therapy were associated with significantly improved survival benefits in OS (p = 0.02) compared with nEA patients. Subgroup analysis indicated that EA patients receiving first-line ICIs showed significantly superior OS compared with nEA patients (p = 0.007). Chemo-ICIs treatment showed significant advantages in terms of OS (p = 0.002) and PFS (p = 0.02) among EA patients compared to nEA patients. In addition, PD-1 inhibitors were associated with improved OS among both EA patients and nEA patients compared with PD-L1 inhibitors. CONCLUSION: EA NSCLC patients who received ICIs-based therapy were associated with significantly improved survival benefits compared with nEA NSCLC patients. Earlier intervention with ICIs and combination treatment was more recommended for EA NSCLC patients. Moreover, PD-1 inhibitors are associated with prolonged survival among both EA and nEA patients.

Correlation between the triglyceride-glucose index and arterial stiffness in Japanese individuals with normoglycaemia: a cross-sectional study.

BACKGROUND: The association between the triglyceride-glucose (TyG) index and arterial stiffness in individuals with normoglycaemia remains unclear. We aimed to evaluate the relationship between the TyG index and arterial stiffness in Japanese individuals with normoglycaemia, providing additional evidence for predicting early arterial stiffness. METHODS: This study included 15,453 adults who participated in the NAGALA Physical Examination Project of the Murakami Memorial Hospital in Gifu, Japan, from 2004 to 2015. Data on clinical demographic characteristics and serum biomarker levels were collected. The TyG index was calculated from the logarithmic transformation of fasting triglycerides multiplied by fasting glucose, and arterial stiffness was measured using the estimated pulse wave velocity calculated based on age and mean blood pressure. The association between the TyG index and arterial stiffness was analysed using a logistic regression model. RESULTS: The prevalence of arterial stiffness was 3.2% (500/15,453). After adjusting for all covariates, the TyG index was positively associated with arterial stiffness as a continuous variable (adjusted odds ratio (OR) = 1.86; 95% Confidence Interval = 1.45-2.39; P<0.001). Using the quartile as the cutoff point, a regression analysis was performed for arterial stiffness when the TyG index was converted into a categorical variable. After adjusting for all covariates, the OR showed an upward trend; the trend test was P<0.001. Subgroup analysis revealed a positive association between the TyG index and arterial stiffness in Japanese individuals with normoglycaemia and different characteristics. CONCLUSION: The TyG index in Japanese individuals with normoglycaemia is significantly correlated with arterial stiffness, and the TyG index may be a predictor of early arterial stiffness.

Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis.

Background: With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis. Objective: This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots. Methods: The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field. Results: A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are "classification", "diagnosis", and "risk". Meanwhile, the current research hotpots were "late gadolinium enhancement" and "carotid ultrasound". Conclusions: AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature based on H&E-stained pathological specimens to accurately predict the clinical benefit of PD-1 inhibitors in ESCC patients. METHODS: ESCC patients receiving PD-1 inhibitors from Shandong Cancer Hospital were included. WSI images of H&E-stained histological specimens of included patients were collected, and randomly divided into training (70%) and validation (30%) sets. The labels of images were defined by the progression-free survival (PFS) with the interval of 4 months. The pretrained ViT model was used for patch-level model training, and all patches were projected into probabilities after linear classifier. Then the most predictive patches were passed to RNN for final patient-level prediction to construct ESCC-pathomics signature (ESCC-PS). Accuracy rate and survival analysis were performed to evaluate the performance of ViT-RNN survival model in validation cohort. RESULTS: 163 ESCC patients receiving PD-1 inhibitors were included for model training. There were 486,188 patches of 1024*1024 pixels from 324 WSI images of H&E-stained histological specimens after image pre-processing. There were 120 patients with 227 images in training cohort and 43 patients with 97 images in validation cohort, with balanced baseline characteristics between two groups. The ESCC-PS achieved an accuracy of 84.5% in the validation cohort, and could distinguish patients into three risk groups with the median PFS of 2.6, 4.5 and 12.9 months (P < 0.001). The multivariate cox analysis revealed ESCC-PS could act as an independent predictor of survival from PD-1 inhibitors (P < 0.001). A combined signature incorporating ESCC-PS and expression of PD-L1 shows significantly improved accuracy in outcome prediction of PD-1 inhibitors compared to ESCC-PS and PD-L1 anlone, with the area under curve value of 0.904, 0.924, 0.610 for 6-month PFS and C-index of 0.814, 0.806, 0.601, respectively. CONCLUSIONS: The outcome supervised pathomics signature based on deep learning has the potential to enable superior prognostic stratification of ESCC patients receiving PD-1 inhibitors, which convert the images pixels to an effective and labour-saving tool to optimize clinical management of ESCC patients.