Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.

BACKGROUND: Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. METHODS: Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. RESULTS: A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. CONCLUSION: The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

The effect of liver metastases on clinical efficacy of first-line programmed death-1 inhibitor plus chemotherapy in esophageal squamous cell carcinoma: A post hoc analysis of ASTRUM-007 and meta-analysis.

OBJECTIVE: To explore the efficacy of serplulimab plus chemotherapy in esophageal squamous cell carcinoma (ESCC) patients with liver metastases. METHODS: A post hoc exploratory analysis of ASTRUM-007 study was performed, focusing on the association between the liver metastases status and the clinical outcomes. A systematic literature search of electronic databases was conducted to identify eligible randomized controlled trials for the meta-analysis. Study-level pooled analyses of hazard ratios (HRs) for PFS according to liver metastases were performed. RESULTS: The post hoc analysis of ASTRUM-007 showed that although patients with liver metastases had a worse prognosis comparing with the non-liver metastases patients in both treatment arms (serplulimab plus chemotherapy arm: median PFS, 5.7 vs. 6.6 months, HR 1.57 [95% CI, 1.15-2.13]; median OS, 13.7 vs. 15.3 months, HR 1.48 [95% CI, 1.09-1.98]; placebo plus chemotherapy arm: median PFS, 4.3 vs. 5.5 months, HR 1.58 [95% CI, 1.01-2.39]; median OS, 10.3 vs. 11.2 months, HR 1.32 [95% CI, 0.84-2.00]), OS and PFS benefits derived from serplulimab plus chemotherapy versus placebo plus chemotherapy in this study were observed in both patients with liver metastases (HR of PFS: 0.60; 95% CI, 0.37-0.97; HR of OS: 0.68; 95% CI, 0.43-1.11) and the non-liver metastases patients (HR of PFS: 0.62; 95% CI, 0.49-0.80; HR of OS: 0.69; 95% CI, 0.55-0.87) with similar magnitude. Three randomized controlled trials were included in the meta-analysis. Pooled HRs demonstrated that the addition of anti-PD-1 antibodies significantly improved PFS compared to chemotherapy alone regardless of liver metastases status. CONCLUSIONS: This study reveals that the presence of liver metastases is a poor prognostic factor but does not affect the improvements in both PFS and OS brought by adding PD-1 blockade to chemotherapy in ESCC patients. Predictive biomarkers for survival in these patients warrant further investigation.

The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.

BACKGROUND & AIMS: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients' historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information. METHODS: This multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n = 3365) and a validation cohort (n = 1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance. RESULTS: NRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/). CONCLUSIONS: Nutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.

Value of the modified Patient-Generated Subjective Global Assessment in indicating the need for nutrition intervention and predicting overall survival in patients with malignant tumors in at least two organs.

BACKGROUND: Although the Patient-Generated Subjective Global Assessment (PG-SGA) is a reference standard used to assess a patient's nutrition status, it is cumbersome to administer. The aim of the present study was to estimate the value of a simpler and easier-to-use modified PG-SGA (mPG-SGA) to evaluate the nutrition status and need for intervention in patients with malignant tumors present in at least two organs. METHODS: A total of 591 patients (343 male and 248 female) were included from the INSCOC study. A Pearson correlation analysis was conducted to assess the correlation between the mPG-SGA and nutrition-related factors, with the optimal cut-off defined by a receiver operating characteristic curve (ROC). The consistency between the mPG-SGA and PG-SGA was compared in a concordance analysis. A survival analysis was used to determine the effects of nutritional intervention among different nutrition status groups. Univariable and multivariable Cox analyses were applied to evaluate the association of the mPG-SGA with the all-cause mortality. RESULTS: The mPG-SGA showed a negative association with nutrition-related factors. Individuals with an mPG-SGA ≥ 5 (rounded from 4.5) were considered to need nutritional intervention. Among the malnourished patients (mPG-SGA ≥ 5), the overall survival (OS) of those who received nutrition intervention was significantly higher than that of patients who did not. However, the OS was not significantly different in the better-nourished patients (mPG-SGA < 5). CONCLUSION: Our findings support that the mPG-SGA is a feasible tool that can be used to guide nutritional interventions and predict the survival of patients with malignant tumors affecting at least two organs.

Integrated neutrophil-to-lymphocyte ratio and handgrip strength better predict survival in patients with cancer cachexia.

OBJECTIVES: Systemic inflammation and skeletal muscle strength play crucial roles in the development and progression of cancer cachexia. In this study we aimed to evaluate the combined prognostic value of neutrophil-to-lymphocyte ratio (NLR) and handgrip strength (HGS) for survival in patients with cancer cachexia. METHODS: This multicenter cohort study involved 1826 patients with cancer cachexia. The NLR-HGS (NH) index was defined as the ratio of neutrophil-to-lymphocyte ratio to handgrip strength. Harrell's C index and receiver operating characteristic (ROC) curve analysis were used to assess the prognosis of NH. Kaplan-Meier analysis and Cox regression models were used to evaluate the association of NH with all-cause mortality. RESULTS: Based on the optimal stratification, 380 women (NH > 0.14) and 249 men (NH > 0.19) were classified as having high NH. NH has shown greater predictive value compared to other indicators in predicting the survival of patients with cancer cachexia according to the 1-, 3-, and 5-y ROC analysis and Harrell's C index calculation. Multivariate survival analysis showed that higher NH was independently associated with an increased risk of death (hazard ratio = 1.654, 95% confidence interval = 1.389-1.969). CONCLUSION: This study demonstrates that the NH index, in combination with NLR and HGS, is an effective predictor of the prognosis of patients with cancer cachexia. It can offer effective prognosis stratification and guidance for their treatment.

Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study.

OBJECTIVES: The concept of possible sarcopenia (PS) was recently introduced to enable timely intervention in settings without the technologies required to make a full diagnosis of sarcopenia. This study aimed to investigate the association between PS and all-cause mortality in patients with solid cancer. DESIGN: Retrospective observational study. SETTING AND PARTICIPANTS: 13,736 patients with 16 types of solid cancer who were ≥18 years old. MEASUREMENTS: The presence of both a low calf circumference (men <34 cm or women <33 cm) and low handgrip strength (men <28 kg or women <18 kg) was considered to indicate PS. Harrell's C-index was used to assess prognostic value and the association of PS with mortality was estimated by calculating multivariable-adjusted hazard ratios (HRs). RESULTS: The study enrolled 7207 men and 6529 women (median age = 57.8 years). During a median follow-up of 43 months, 3150 deaths occurred. PS showed higher Harrell's C-index (0.549, 95%CI = [0.541, 0.557]) than the low calf circumference (0.541, 95%CI = [0.531, 0.551], P = 0.037) or low handgrip strength (0.542, 95%CI = [0.532, 0.552], P = 0.026). PS was associated with increased mortality risk in both univariate (HR = 1.587, 95%CI = [1.476, 1.708]) and multivariable-adjusted models (HR = 1.190, 95%CI = [1.094, 1.293]). Sensitivity analyses showed that the association of PS with mortality was robust in different covariate subgroups, which also held after excluding those patients who died within the first 3 months (HR = 1.162, 95%CI = [1.060, 1.273]), 6 months (HR = 1.150, 95%CI = [1.039, 1.274]) and 12 months (HR = 1.139, 95%CI = [1.002, 1.296]) after enrollment. CONCLUSION: PS could independently and robustly predict all-cause mortality in patients with solid cancer. These findings imply the importance of including PS assessment in routine cancer care to provide significant prognostic information to help mitigate sarcopenia-related premature deaths.

Modified black phosphorus quantum dots promotes spinal cord injury repair by targeting the AKT signaling pathway.

Spinal cord injury (SCI) is a serious refractory disease of the central nervous system (CNS), which mostly caused by high-energy trauma. Existing interventions such as hormone shock and surgery are insufficient options, which relate to the secondary inflammation and neuronal dysfunction. Hydrogel with neuron-protective behaviors attracts tremendous attention, and black phosphorus quantum dots (BPQDs) encapsulating with Epigallocatechin-3-gallate (EGCG) hydrogels (E@BP) is designed for inflammatory modulation and SCI treatment in this study. E@BP displays good stability, biocompatibility and safety profiles. E@BP incubation alleviates lipopolysaccharide (LPS)-induced inflammation of primary neurons and enhances neuronal regeneration in vitro. Furthermore, E@BP reconstructs structural versus functional integrity of spinal cord tracts, which promotes recovery of motor neuron function in SCI rats after transplantation. Importantly, E@BP restarts the cell cycle and induces nerve regeneration. Moreover, E@BP diminishes local inflammation of SCI tissues, characterized by reducing accumulation of astrocyte, microglia, macrophages, and oligodendrocytes. Indeed, a common underlying mechanism of E@BP regulating neural regenerative and inflammatory responses is to promote the phosphorylation of key proteins related to AKT signaling pathway. Together, E@BP probably repairs SCI by reducing inflammation and promoting neuronal regeneration via the AKT signaling pathway.

First-line serplulimab or placebo plus chemotherapy in PD-L1-positive esophageal squamous cell carcinoma: a randomized, double-blind phase 3 trial.

First-line systemic therapeutic options for advanced esophageal squamous cell carcinoma (ESCC) are limited. In this multicenter, double-blind phase 3 trial, a total of 551 patients with previously untreated, locally advanced or metastatic ESCC and PD-L1 combined positive score of ≥1 were randomized (2:1) to receive serplulimab (an anti-PD-1 antibody; 3 mg/kg) or placebo (on day 1), plus cisplatin (50 mg/m2) (on day 1) and continuous infusion of 5-fluorouracil (1,200 mg/m2) (on days 1 and 2), once every 2 weeks. The study met the primary endpoints. At the prespecified final analysis of progression-free survival (PFS) assessed by the blinded independent radiological review committee, serplulimab plus chemotherapy significantly improved PFS compared with placebo plus chemotherapy (median PFS of 5.8 months and 5.3 months, respectively; hazard ratio, 0.60; 95% confidence interval, 0.48-0.75; P < 0.0001). At the prespecified interim analysis of overall survival (OS), serplulimab plus chemotherapy also significantly prolonged OS compared with placebo plus chemotherapy (median OS of 15.3 months and 11.8 months, respectively; hazard ratio, 0.68; 95% confidence interval, 0.53-0.87; P = 0.0020). Grade 3 or higher treatment-related adverse events occurred in 201 (53%) and 81 (48%) patients in the serplulimab plus chemotherapy group and the placebo plus chemotherapy group, respectively. Serplulimab plus chemotherapy administered every 2 weeks significantly improved PFS and OS in patients with previously untreated, PD-L1-positive advanced ESCC, with a manageable safety profile. This study is registered with ClinicalTrials.gov ( NCT03958890 ).

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting.

Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within E. coli and yeast. The test accuracy for different culture times and cell cycles of E. coli was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of E. coli growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering.

Chronic Inflammation Pathway NF-κB Cooperates with Epigenetic Reprogramming to Drive the Malignant Progression of Glioblastoma.

Without an effective strategy for targeted therapy, glioblastoma is still incurable with a median survival of only 15 months. Both chronic inflammation and epigenetic reprogramming are hallmarks of cancer. However, the mechanisms and consequences of their cooperation in glioblastoma remain unknown. Here, we discover that chronic inflammation governs H3K27me3 reprogramming in glioblastoma through the canonical NF-κB pathway to target EZH2. Being a crucial mediator of chronic inflammation, the canonical NF-κB signalling specifically directs the expression and redistribution of H3K27me3 but not H3K4me3, H3K9me3 and H3K36me3. Using RNA-seq screening to focus on genes encoding methyltransferases and demethylases of histone, we identify EZH2 as a key methyltransferase to control inflammation-triggered epigenetic reprogramming in gliomagenesis. Mechanistically, NF-κB selectively drives the expression of EZH2 by activating its transcription, consequently resulting in a global change in H3K27me3 expression and distribution. Furthermore, we find that co-activation of NF-κB and EZH2 confers the poorest clinical outcome, and that the risk for glioblastoma can be accurately molecularly stratified by NF-κB and EZH2. It is notable that NF-κB can potentially cooperate with EZH2 in more than one way, and most importantly, we demonstrate a Synergistic effect of cancer cells induced by combinatory inhibition of NF-κB and EZH2, which both are frequently over-activated in glioblastoma. In summary, we uncover a functional cooperation between chronic inflammation and epigenetic reprogramming in glioblastoma, combined targeting of which by inhibitors guaranteed in safety and availability furnishes a potent strategy for effective treatment of this fatal disease.

Engineering by Cuts: How Kirigami Principle Enables Unique Mechanical Properties and Functionalities.

Kirigami, the ancient art of paper cutting, has evolved into a design and fabrication framework to engineer multi-functional materials and structures at vastly different scales. By slit cutting with carefully designed geometries, desirable mechanical behaviors-such as accurate shape morphing, tunable auxetics, super-stretchability, buckling, and multistability-can be imparted to otherwise inflexible sheet materials. In addition, the kirigami sheet provides a versatile platform for embedding different electronic and responsive components, opening up avenues for building the next generations of metamaterials, sensors, and soft robotics. These promising potentials of kirigami-based engineering have inspired vigorous research activities over the past few years, generating many academic publications. Therefore, this review aims to provide insights into the recent advance in this vibrant field. In particular, this paper offers the first comprehensive survey of unique mechanical properties induced by kirigami cutting, their underlying physical principles, and their corresponding applications. The synergies between design methodologies, mechanics modeling, advanced fabrication, and material science will continue to mature this promising discipline.

De novo Creation and Assessment of a Prognostic Fat-Age-Inflammation Index "FAIN" in Patients With Cancer: A Multicenter Cohort Study.

Background and Aims: Malnutrition is highly prevalent and is related to multiple impaired clinical outcomes in cancer patients. This study aimed to de novo create an objective, nutrition-related index specially for prognostic purposes in oncology populations. Methods: We performed a multicenter cohort study including 14,134 cancer patients. The prognostic impact for each baseline characteristic was estimated by calculating Harrell's C-index. The optimal parameters reflecting the nutritional and inflammatory impact on patients' overall survival were selected to develop the fat-age-inflammation (FAIN) index. The associations of the FAIN with the nutritional status, physical performance, quality of life, short-term outcomes and mortality of patients were comprehensively evaluated. Independent external validation was performed to further assess the prognostic value of the FAIN. Results: The study enrolled 7,468 men and 6,666 women with a median age of 57 years and a median follow-up of 42 months. The FAIN index was defined as: (triceps skinfold thickness + albumin) / [age + 5 × (neutrophil count/lymphocyte count)]. There were significant associations of the FAIN with the nutritional status, physical performance, quality of life and short-term outcomes. The FAIN also showed better discrimination performance than the Nutritional Risk Index, the Prognostic Nutritional Index and the Controlling Nutritional Status index (all P < 0.05). In multivariable-adjusted models, the FAIN was independently associated with a reduced death hazard both as a continuous variable (HR = 0.57, 95%CI = 0.47-0.68) and per one standard deviation (HR = 0.83, 95%CI = 0.78-0.88). External validation in a multicenter lung cancer cohort (n = 227) further confirmed the prognostic value of the FAIN. Conclusions: This study created and assessed the prognostic FAIN index, which might act as a feasible option to monitor the nutritional status and help develop intervention strategies to optimize the survival outcomes of cancer patients.

Nuclear Aurora kinase A switches m6A reader YTHDC1 to enhance an oncogenic RNA splicing of tumor suppressor RBM4.

Aberrant RNA splicing produces alternative isoforms of genes to facilitate tumor progression, yet how this process is regulated by oncogenic signal remains largely unknown. Here, we unveil that non-canonical activation of nuclear AURKA promotes an oncogenic RNA splicing of tumor suppressor RBM4 directed by m6A reader YTHDC1 in lung cancer. Nuclear translocation of AURKA is a prerequisite for RNA aberrant splicing, specifically triggering RBM4 splicing from the full isoform (RBM4-FL) to the short isoform (RBM4-S) in a kinase-independent manner. RBM4-S functions as a tumor promoter by abolishing RBM4-FL-mediated inhibition of the activity of the SRSF1-mTORC1 signaling pathway. Mechanistically, AURKA disrupts the binding of SRSF3 to YTHDC1, resulting in the inhibition of RBM4-FL production induced by the m6A-YTHDC1-SRSF3 complex. In turn, AURKA recruits hnRNP K to YTHDC1, leading to an m6A-YTHDC1-hnRNP K-dependent exon skipping to produce RBM4-S. Importantly, the small molecules that block AURKA nuclear translocation, reverse the oncogenic splicing of RBM4 and significantly suppress lung tumor progression. Together, our study unveils a previously unappreciated role of nuclear AURKA in m6A reader YTHDC1-dependent oncogenic RNA splicing switch, providing a novel therapeutic route to target nuclear oncogenic events.

Whole-exome sequencing identified novel variants in three Chinese Leigh syndrome pedigrees.

Leigh syndrome (LS), the most common mitochondrial disease in early childhood, usually manifests variable neurodegenerative symptoms and typical brain magnetic resonance imaging (MRI) lesions. To date, pathogenic variants in more than 80 genes have been identified. However, there are still many cases without molecular diagnoses, and thus more disease-causing variants need to be unveiled. Here, we presented three clinically suspected LS patients manifesting neurological symptoms including developmental delay, hypotonia, and epilepsy during the first year of age, along with symmetric brain lesions on MRI. We explored disease-associated variants in patients and their nonconsanguineous parents by whole-exome sequencing and subsequent Sanger sequencing verification. Sequencing data revealed three pairs of disease-associated compound heterozygous variants: c.1A>G (p.Met1?) and 409G>C (p.Asp137His) in SDHA, c.1253G>A (p.Arg418His) and 1300C>T (p.Leu434Phe) in NARS2, and c.5C>T (p.Ala2Val) and 773T>G (p.Leu258Trp) in ECHS1. Among them, the likely pathogenic variants c.409G>C (p.Asp137His) in SDHA, c.1300C>T (p.Leu434Phe) in NARS2, and c.773T>G (p.Leu258Trp) in ECHS1 were newly identified. Segregation analysis indicated the possible disease-causing nature of the novel variants. In silico prediction and three-dimensional protein modeling further suggested the potential pathogenicity of these variants. Our discovery of novel variants expands the gene variant spectrum of LS and provides novel evidence for genetic counseling.

Harnessing the Multistability of Kresling Origami for Reconfigurable Articulation in Soft Robotic Arms.

This study examines a biology-inspired approach of using reconfigurable articulation to reduce the control requirement for soft robotic arms. We construct a robotic arm by assembling Kresling origami modules that exhibit predictable bistability. By switching between their two stable states, these origami modules can behave either like a flexible joint with low bending stiffness or like a stiff link with high stiffness, without requiring any continuous power supply. In this way, the robotic arm can exhibit pseudo-linkage kinematics with lower control requirements and improved motion accuracy. A unique advantage of using origami as the robotic arm skeleton is that its bending stiffness ratio between stable states is directly related to the underlying Kresling design. Therefore, we conduct extensive parametric analyses and experimental validations to identify the optimized Kresling pattern for articulation. The results indicate that a higher angle ratio, a smaller resting length at contracted stable state, and a large number of polygon sides can offer more significant and robust bending stiffness tuning. Based on this insight, we construct a proof-of-concept, tendon-driven robotic arm consisting of three modules and show that it can exhibit the desired reconfigurable articulation behavior. Moreover, the deformations of this manipulator are consistent with kinematic model predictions, which validate the possibility of using simple controllers for such compliant robotic systems.

Several anthropometric measurements and cancer mortality: predictor screening, threshold determination, and joint analysis in a multicenter cohort of 12138 adults.

BACKGROUND: Anthropometric measurements (AMs) are cost-effective surrogates for evaluating body size. This study aimed to identify the optimal prognostic AMs, their thresholds, and their joint associations with cancer mortality. METHODS: We performed an observational cohort study including 12138 patients with cancer at five institutions in China. Information on demographics, disease, nutritional status, and AMs, including the body mass index, mid-arm muscle circumference, mid-arm circumference, handgrip strength, calf circumference (CC), and triceps-skinfold thickness (TSF), was collected and screened as mortality predictors. The optimal stratification was used to determine the thresholds to categorize those prognostic AMs, and their associations with mortality were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HRs). RESULTS: The study included 5744 females and 6394 males with a mean age of 56.9 years. The CC and TSF were identified as better mortality predictors than other AMs. The optimal thresholds were women 30 cm and men 32.8 cm for the CC, and women 21.8 mm and men 13.6 mm for the TSF. Patients in the low CC or low TSF group had a 13% (HR = 1.13, 95% CI = 1.03-1.23) and 22% (HR = 1.22, 95% CI = 1.12-1.32) greater mortality risk compared with their normal CC/TSF counterparties, respectively. Concurrent low CC and low TSF showed potential joint effect on mortality risk (HR = 1.39, 95% CI = 1.25-1.55). CONCLUSIONS: These findings support the importance of assessing the CC and TSF simultaneously in hospitalized cancer patients to guide interventions to optimize their long-term outcomes.

Pre-injection of Zebrafish (Danio rerio) tnfb Polyclonal Antibody Decreases the Mortality of Vibrio vulnificus Infected Zebrafish.

Tumor necrosis factor (TNF) plays an important role in an inflammatory cytokine storm. Over-secretion of TNF by the host in response to infection aggravates the disease. TNF expression level is positively correlated with the mortality caused by some bacterial infections. Therefore, using TNF antibody may alleviate the inflammation to resist bacterial infections. The function of fish TNF-b antibody in bacterial infection is still unclear. In this study, infection models of Vibrio vulnificus FJ03-X2 strain with high pathogenicity and strong virulence were established in zebrafish (Danio rerio) fibroblast cell line (ZF4 cells) and zebrafish. Zebrafish tnfb (Zetnf-b) gene was cloned and expressed by Escherichia coli BL21 (DE3), and Zetnf-b polyclonal antibody was prepared. Pre-injection of Zetnf-b polyclonal antibody and AG-126 before infecting with V. vulnificus could increase the survival rate of zebrafish by 36.6 and 46.7%, respectively. Pre-injection of Zetnf-b polyclonal antibody could effectively decrease the mortality of zebrafish infected by V. vulnificus. Thus, TNF polyclonal antibody therapy could be considered as an effective strategy to control V. vulnificus in fish.

Pneumatic Soft Actuators With Kirigami Skins.

Soft pneumatic actuators have become indispensable for many robotic applications due to their reliability, safety, and design flexibility. However, the currently available actuator designs can be challenging to fabricate, requiring labor-intensive and time-consuming processes like reinforcing fiber wrapping and elastomer curing. To address this issue, we propose to use simple-to-fabricate kirigami skins-plastic sleeves with carefully arranged slit cuts-to construct pneumatic actuators with pre-programmable motion capabilities. Such kirigami skin, wrapped outside a cylindrical balloon, can transform the volumetric expansion from pneumatic pressure into anisotropic stretching and shearing, creating a combination of axial extension and twisting in the actuator. Moreover, the kirigami skin exhibits out-of-plane buckling near the slit cut, which enables high stretchability. To capture such complex deformations, we formulate and experimentally validates a new kinematics model to uncover the linkage between the kirigami cutting pattern design and the actuator's motion characteristics. This model uses a virtual fold and rigid-facet assumption to simplify the motion analysis without sacrificing accuracy. Moreover, we tested the pressure-stroke performance and elastoplastic behaviors of the kirigami-skinned actuator to establish an operation protocol for repeatable performance. Analytical and experimental parametric analysis shows that one can effectively pre-program the actuator's motion performance, with considerable freedom, simply by adjusting the angle and length of the slit cuts. The results of this study can establish the design and analysis framework for a new family of kirigami-skinned pneumatic actuators for many robotic applications.

A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data.

BACKGROUND AND AIMS: Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This study established a machine learning (ML)-based, individualized decision system to identify and grade malnutrition using large-scale data from cancer patients. METHODS: This was an observational, nationwide, multicenter cohort study that included 14134 cancer patients from five institutions in four different geographic regions of China. Multi-stage K-means clustering was performed to isolate and grade malnutrition based on 17 core nutritional features. The effectiveness of the identified clusters for reflecting clinical characteristics, nutritional status and patient outcomes was comprehensively evaluated. The study population was randomly split for model derivation and validation. Multiple ML algorithms were developed, validated and compared to screen for optimal models to implement the cluster prediction. RESULTS: A well-nourished cluster (n = 8193, 58.0%) and a malnourished cluster with three phenotype-specific severity levels (mild = 2195, 15.5%; moderate = 2491, 17.6%; severe = 1255, 8.9%) were identified. The clusters showed moderate agreement with the Patient-Generated Subjective Global Assessment and the Global Leadership Initiative on Malnutrition criteria. The severity of malnutrition was negatively associated with the nutritional status, physical status, quality of life, and short-term outcomes, and was monotonically correlated with reduced overall survival. A multinomial logistic regression was found to be the optimal ML algorithm, and models built based on this algorithm showed almost perfect performance to predict the clusters in the validation data. CONCLUSIONS: This study developed a fusion decision system that can be used to facilitate the identification and severity grading of malnutrition in patients with cancer. Moreover, the study workflow is flexible, and might provide a generalizable solution for the artificial intelligence-based assessment of malnutrition in a wider variety of scenarios.

Physical reservoir computing with origami and its application to robotic crawling.

A new paradigm called physical reservoir computing has recently emerged, where the nonlinear dynamics of high-dimensional and fixed physical systems are harnessed as a computational resource to achieve complex tasks. Via extensive simulations based on a dynamic truss-frame model, this study shows that an origami structure can perform as a dynamic reservoir with sufficient computing power to emulate high-order nonlinear systems, generate stable limit cycles, and modulate outputs according to dynamic inputs. This study also uncovers the linkages between the origami reservoir's physical designs and its computing power, offering a guideline to optimize the computing performance. Comprehensive parametric studies show that selecting optimal feedback crease distribution and fine-tuning the underlying origami folding designs are the most effective approach to improve computing performance. Furthermore, this study shows how origami's physical reservoir computing power can apply to soft robotic control problems by a case study of earthworm-like peristaltic crawling without traditional controllers. These results can pave the way for origami-based robots with embodied mechanical intelligence.