CT-based muscle and adipose measurements predict prognosis in patients with digestive system malignancy.

The role of skeletal muscle and adipose tissue in the progression of cancer has been gradually discussed, but it needs further exploration. The objective of this study was to provide an in-depth analysis of skeletal muscle and fat in digestive malignancies and to construct novel predictors for clinical management. This is a retrospective study that includes data from Cancer Center, the First Hospital of Jilin University. Basic characteristic information was analyzed by T tests. Correlation matrices were drawn to explore the relationship between CT-related indicators and other indicators. Cox risk regression analyses were performed to analyze the association between the overall survivals (OS) and various types of indicators. A new indicator body composition score (BCS) was then created and a time-dependent receiver operating characteristic curve was plotted to analyze the efficacy of the BCS. Finally, a nomogram was produced to develop a scored-CT system based on BCS and other indicators. C-index and calibration curve analyses were performed to validate the predictive accuracy of the scored-CT system. A total of 575 participants were enrolled in the study. Cox risk regression model revealed that VFD, L3 SMI and VFA/SFA were associated with prognosis of cancer patients. After adjustment, BCS index based on CT was significantly associated with prognosis, both in all study population and in subgroup analysis according to tumor types (all study population: HR 2.036, P < 0.001; colorectal cancer: HR 2.693, P < 0.001; hepatocellular carcinoma: HR 4.863, P < 0.001; esophageal cancer: HR 4.431, P = 0.008; pancreatic cancer: HR 1.905, P = 0.016; biliary system malignancies: HR 23.829, P = 0.035). The scored-CT system was constructed according to tumor type, stage, KPS, PG-SGA and BCS index, and it was of great predictive validity. This study identified VFD, L3 SMI and VFA/SFA associated with digestive malignancies outcomes. BCS was created and the scored-CT system was established to predict the OS of cancer patients.

Association Between Cardiovascular Health and Retinopathy in US Adults: From NHANES 2005-2008.

PURPOSE: Investigating the relationship between cardiovascular health (CVH) and retinopathy in the adult population of the United States. DESIGN: The cross-sectional study. METHODS: The study utilized samples, including the diabetes population, from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2008 (N= 4249), to assess cardiovascular health (CVH) using the Life's Essential 8 (LE8) assessment. Retinopathy is determined through imaging assessment by professionals independently grading fundus photographs. Univariable and multivariable weighted logistic regression models, restricted cubic splines (RCS), subgroup analysis and weighted quantile sum (WQS) regression approaches were employed to assess the association between LE8 score-based CVH status and retinopathy. The mediation analysis was conducted to investigate whether serum albumin levels mediated the relationship between LE8 score and retinopathy. RESULTS: In a fully adjusted logistic regression model, participants in the moderate and high CVH groups had a 39% (odds ratio (OR) 0.61, 95% confidence interval (CI) 0.43-0.87, P-value = 0.01) and 56% (OR 0.44, 95% CI 0.25-0.77, P-value < 0.001) lower odds of developing retinopathy compared to the low CVH group. The RCS model indicates a significant non-linear relationship between CVH and retinopathy. The WQS regression analysis suggests that blood glucose (47.65%) and blood pressure (19.41%) have the highest weights in relation to retinopathy. Mediation analysis suggests that serum albumin partially mediates the relationship between LE8 scores and retinopathy. CONCLUSION: This study demonstrates a significant negative correlation between overall cardiovascular health measured by LE8 scores and retinopathy. Public health strategies that promote achieving optimal cardiovascular health indicators may help reduce the burden of retinal microvascular diseases.

[Research progress on mechanism of traditional Chinese medicine intervention in angiogenesis in prevention and treatment of nontraumatic avascular necrosis of femoral head].

Nontraumatic avascular necrosis of the femoral head(NANFH) is a common and refractory femoral head disease that causes bone death due to interruption of blood supply. Early clinical symptoms are atypical, such as hip pain and limited joint function. In the late stage, severe pain, shortening of the affected limb, claudication, and other serious symptoms are common, which se-riously affects the quality of life of patients. Therefore, it is of great significance to actively improve the clinical symptoms of NANFH to enhance the quality of life of patients. The pathogenesis of NANFH is complex, such as traumatic vascular circulatory disorders, the use of hormones or other drugs, alcoholism, and diabetes mellitus. These factors directly or indirectly lead to femoral head vascular damage, thrombosis, and coagulation system disorders, which reduce the blood supply to the acetabulum and femoral head, thus causing ischaemic death of the femoral head or even femoral head collapse. NANFH is mainly categorized as "bone impotence" and "bone paralysis" in traditional Chinese medicine(TCM). The treatment of NANFH with TCM has the characteristics and advantages of a long history, stable and reliable therapeutic effect, fewer adverse reactions, good patient tolerance, and high acceptance. Previous studies have shown that the promotion of angiogenesis is a key initiative in the prevention and treatment of NANFH, and TCM can promote fe-moral head angiogenesis by interfering with the expression of angiogenesis-related factors, which in turn can help to restore the blood supply of the femoral head and thus improve clinical symptoms of NANFH and prevent and treat NANFH. This article described the roles of blood supply interruption and angiogenesis in NANFH and the accumulated knowledge and experience of TCM in NANFH and summarized the role of angiogenesis-related factors in NANFH and the research progress on TCM intervention, so as to provide an idea for the subsequent research and a new basis for the clinical application of TCM in the treatment of NANFH.

A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma.

Lung cancer has seriously threatened human health due to its high lethality and morbidity. Lung adenocarcinoma, in particular, is one of the most common subtypes of lung cancer. Pathological diagnosis is regarded as the gold standard for cancer diagnosis. However, the traditional manual screening of lung cancer pathology images is time consuming and error prone. Computer-aided diagnostic systems have emerged to solve this problem. Current research methods are unable to fully exploit the beneficial features inherent within patches, and they are characterized by high model complexity and significant computational effort. In this study, a deep learning framework called Multi-Scale Network (MSNet) is proposed for the automatic detection of lung adenocarcinoma pathology images. MSNet is designed to efficiently harness the valuable features within data patches, while simultaneously reducing model complexity, computational demands, and storage space requirements. The MSNet framework employs a dual data stream input method. In this input method, MSNet combines Swin Transformer and MLP-Mixer models to address global information between patches and the local information within each patch. Subsequently, MSNet uses the Multilayer Perceptron (MLP) module to fuse local and global features and perform classification to output the final detection results. In addition, a dataset of lung adenocarcinoma pathology images containing three categories is created for training and testing the MSNet framework. Experimental results show that the diagnostic accuracy of MSNet for lung adenocarcinoma pathology images is 96.55 %. In summary, MSNet has high classification performance and shows effectiveness and potential in the classification of lung adenocarcinoma pathology images.

Revisiting the mechanisms linking blood glucose to cognitive impairment: new evidence for the potential important role of klotho.

Background: The association between blood glucose and cognition is controversial. Klotho is an anti-aging protein with neural protective effects. This study aimed to use a population-based study to disentangle the relationship between blood glucose levels and cognitive function in older adults, and to explore the role of klotho in it. Methods: A total of 1445 eligible participants from National Health and Nutrition Examination Survey (NHANES) 2011-2014 were included in our study. Cognitive function was assessed by Digit Symbol Substitution Test (DSST) and categorized into four quartiles (Q1-Q4). General characteristics and laboratory test results including serum klotho concentration and blood glucose levels were collected. Associations of cognitive function and klotho levels with blood glucose concentrations were explored through multivariate linear regression models. Mediation models were constructed to figure out the mediating role of klotho. Results: All three multivariate linear regression models showed a negative correlation between blood glucose and cognitive function. (Model 1, ß=-0.149, 95%CI: -0.202,-0.096, p=0.001; Model 2, ß=-0.116, 95%CI: -0.167,-0.065, p=0.001; Model 3, ß=-0.007, 95%CI: -0.118,-0.023, p=0.003). Mediation analysis showed that klotho mediated the statistical association between blood glucose level and cognitive function with proportions (%) of 12.5. Conclusion: Higher blood glucose levels are associated with poorer cognitive performance in non-diabetic older adults, partially mediated through lower klotho levels.

Non-coding RNAs are involved in tumor cell death and affect tumorigenesis, progression, and treatment: a systematic review.

Cell death is ubiquitous during development and throughout life and is a genetically determined active and ordered process that plays a crucial role in regulating homeostasis. Cell death includes regulated cell death and non-programmed cell death, and the common types of regulatory cell death are necrosis, apoptosis, necroptosis, autophagy, ferroptosis, and pyroptosis. Apoptosis, Necrosis and necroptosis are more common than autophagy, ferroptosis and pyroptosis among cell death. Non-coding RNAs are regulatory RNA molecules that do not encode proteins and include mainly microRNAs, long non-coding RNAs, and circular RNAs. Non-coding RNAs can act as oncogenes and tumor suppressor genes, with significant effects on tumor occurrence and development, and they can also regulate tumor cell autophagy, ferroptosis, and pyroptosis at the transcriptional or post-transcriptional level. This paper reviews the recent research progress on the effects of the non-coding RNAs involved in autophagy, ferroptosis, and pyroptosis on tumorigenesis, tumor development, and treatment, and looks forward to the future direction of this field, which will help to elucidate the molecular mechanisms of tumorigenesis and tumor development, as well as provide a new vision for the treatment of tumors.

Aerobic granular sludge for complex heavy metal-containing wastewater treatment: characterization, performance, and mechanisms analysis.

Complex heavy metal (HM)-containing wastewater discharges pose substantial risks to global water ecosystems and human health. Aerobic granular sludge (AGS) has attracted increased attention as an efficient and low-cost adsorbent in HM-containing wastewater treatment. Therefore, this study systematically evaluates the effect of Cu(II), Ni(II), and Cr(III) addition on the characteristics, performance and mechanism of AGS in complex HM-containing wastewater treatment process by means of fourier transform infrared spectroscopy, inductively coupled plasma spectrocopcy, confocal laser scanning microscopy, extracellular polymeric substances (EPS) fractions detection and scanning electron microscope-energy dispersive X-ray. The results showed that AGS efficiently eliminated Cu(II), Ni(II), and Cr(III) by the orchestrated mechanisms of ion exchange, three-layer EPS adsorption [soluble microbial products EPS (SMP-EPS), loosely bound EPS (LB-EPS), tightly bound EPS (TB-EPS)], and inner-sphere adsorption; notably, almost 100% of Ni(II) was removed. Three-layer EPS adsorption was the dominant mechanism through which the HM were removed, followed by ion exchange and inner-sphere adsorption. SMP-EPS and TB-EPS were identified as the key EPS fractions for adsorbing Cr(III) and Cu(II), respectively, while Ni(II) was adsorbed evenly on SMP-EPS, TB-EPS, and LB-EPS. Moreover, the rates at which the complex HM penetrated into the granule interior and their affinity for EPS followed the order Cu(II) > Ni(II) > Cr(III). Ultimately, addition of complex HM stimulated microorganisms to excrete massive phosphodiesterases (PDEs), leading to a pronounced decrease in cyclic diguanylate (c-di-GMP) levels, which subsequently suppressed EPS secretion due to the direct linkage between c-di-GMP and EPS. This study unveils the adaptability and removal mechanism of AGS in the treatment of complex HM-containing wastewater, which is expected to provide novel insights for addressing the challenges posed by intricate real wastewater scenarios.

Composition Analysis of the Solid Electrolyte Interface of NaK Anodes in Na-Ion Batteries.

Sodium-ion batteries have emerged as a promising alternative to Li-ion batteries due to the abundance of sodium. However, anodes in Na-ion batteries face challenges such as dendrite formation and an unstable solid electrolyte interface layer. To address these challenges, NaK liquid metal alloy anodes have been proposed as an alternative because they do not form dendrites. In our study, we demonstrate that the NaK alloy anode interacts with the commonly used ethylene carbonate and dimethyl carbonate electrolyte, leading to a continuously growing unstable SEI layer, evidenced by cycling failures under 100 cycles and an increasing charge transfer resistance in electrochemical impedance spectroscopy studies. In situ surface-enhanced Raman spectroscopy and X-ray photoelectron spectroscopy reveal that over the course of cycling the surface of the NaK anode becomes increasingly sodium-rich. After 30 cycles, XPS analysis detects only trace amounts of potassium on the NaK anode surface. When the electrolyte is analyzed postcycling using inductively coupled plasma optical emission spectroscopy, there is a noticeable increase in potassium levels, suggesting that potassium metal dissolves into the electrolyte. The introduction of a 10 wt % fluoroethylene carbonate additive can mitigate this problem to some extent, enabling an enhanced cycling performance of up to 800 cycles at 1C. Nevertheless, the dissolution of K metal is still evident in the XPS results, albeit to a lesser degree. These discoveries provide valuable insights for designing a more robust SEI layer for the NaK anode.

Associations of multiple toxic metal exposures with metabolic dysfunction-associated fatty liver disease: NHANES 2011-2018.

Background: The occurrence of metabolic dysfunction-associated fatty liver disease (MASLD) is driven by multiple factors including obesity, hypertension, dyslipidemia, and insulin resistance. However, epidemiological research investigating the association between metal exposure and MASLD occurrence remains limited. Methods: We conducted a large cross-sectional study with 6,520 participants who were involved in the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2018. Using generalized linear regression, we examined the relationship between five heavy metals (mercury, manganese, lead, selenium, cadmium) and MASLD. Furthermore, restricted cubic spline models and weighted quantile sum (WQS) analysis were employed to characterize the exposure-response relationship between the five metals and MASLD. Results: Higher blood selenium levels were associated with an increased likelihood of MASLD among US adults. Blood lead exposure was also positively correlated with MASLD risk. However, there was no significant association observed between blood cadmium, mercury, manganese levels, and MASLD risk. Among the five metals, blood cadmium exposure accounted for the highest proportion of MASLD risk. Conclusion: Our study indicated the significant association between blood cadmium and lead exposure levels and the occurrence of MASLD in a representative sample of US adults.

Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks.

Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of computational models for PPIs prediction have been developed because experimental methods are slow and expensive. Most models employ a sliding window approach in which local neighbors are concatenated to present a target residue. However, those neighbors are not distinguished by pairwise information between a neighbor and the target. In this study, we propose a novel PPIs prediction model AttCNNPPISP, which combines attention mechanism and convolutional neural networks (CNNs). The attention mechanism dynamically captures the pairwise correlation of each neighbor-target pair within a sliding window, and therefore makes a better understanding of the local environment of target residue. And then, CNNs take the local representation as input to make prediction. Experiments are employed on several public benchmark datasets. Compared with the state-of-the-art models, AttCNNPPISP improves the prediction performance. Also, the experimental results demonstrate that the attention mechanism is effective in terms of constructing comprehensive context information of target residue.

Progress in research on tumor microenvironment-based spatial omics technologies.

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information. It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results. Spatial omics technologies include spatial transcriptomics, spatial proteomics, spatial metabolomics, and other technologies, the most widely used of which are spatial transcriptomics and spatial proteomics. The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist, including the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix. A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment, which can reveal problems that conventional research techniques cannot, potentially leading to the development of novel therapeutic agents for cancer. This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

Towards scanning nanostructure X-ray microscopy.

This article demonstrates spatial mapping of the local and nanoscale structure of thin film objects using spatially resolved pair distribution function (PDF) analysis of synchrotron X-ray diffraction data. This is exemplified in a lab-on-chip combinatorial array of sample spots containing catalytically interesting nanoparticles deposited from liquid precursors using an ink-jet liquid-handling system. A software implementation is presented of the whole protocol, including an approach for automated data acquisition and analysis using the atomic PDF method. The protocol software can handle semi-automated data reduction, normalization and modeling, with user-defined recipes generating a comprehensive collection of metadata and analysis results. By slicing the collection using included functions, it is possible to build images of different contrast features chosen by the user, giving insights into different aspects of the local structure.

The role of lysosomal peptidases in glioma immune escape: underlying mechanisms and therapeutic strategies.

Glioblastoma is the most common primary malignant tumor of the central nervous system, which has the characteristics of strong invasion, frequent recurrence, and rapid progression. These characteristics are inseparable from the evasion of glioma cells from immune killing, which makes immune escape a great obstacle to the treatment of glioma, and studies have confirmed that glioma patients with immune escape tend to have poor prognosis. The lysosomal peptidase lysosome family plays an important role in the immune escape process of glioma, which mainly includes aspartic acid cathepsin, serine cathepsin, asparagine endopeptidases, and cysteine cathepsins. Among them, the cysteine cathepsin family plays a prominent role in the immune escape of glioma. Numerous studies have confirmed that glioma immune escape mediated by lysosomal peptidases has something to do with autophagy, cell signaling pathways, immune cells, cytokines, and other mechanisms, especially lysosome organization. The relationship between protease and autophagy is more complicated, and the current research is neither complete nor in-depth. Therefore, this article reviews how lysosomal peptidases mediate the immune escape of glioma through the above mechanisms and explores the possibility of lysosomal peptidases as a target of glioma immunotherapy.

Group Replacement-Rearrangement-Triggered Linear-Assembly Nonaqueous Precipitation Synthesis of Hydroxyapatite Fibers.

A novel method for hydroxyapatite fiber preparation with highly large-scale production prospects is of paramount importance but remains particularly difficult. Here, group replacement-rearrangement-triggered linear-assembly nonaqueous precipitation synthesis has been proposed for hydroxyapatite fibers under mild conditions. Pure hydroxyapatite fibers can be fabricated taking disodium hydrogen phosphate, calcium acetate, and glycerol as the phosphorus source, calcium source, and solvent, respectively. Single hexagonal crystal structures of hydroxyapatite fibers growing along the c-axis and preferential growth of the (002) crystal plane similar to the layered stacking structure of an adult bone have been confirmed by XRD refinement tests and calculation, TEM electron diffraction calibration, and FE-SEM. Highly active carbonate apatite is further demonstrated by EDS, FT-IR, Raman spectroscopy, and XPS. Unsaturated P-O and O-Ca bonds at both ends of the hexagonal-sheet assembly unit in a high-polarity nonaqueous glycerol environment without strongly coordinated OH- confirm the solution spontaneous linear assembly to form the single hydroxyapatite fibers.

A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes.

Objective: Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries. Methods: This paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classifification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM. Results: The MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM. Conclusions: In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks.

Association of Neutrophil-to-Lymphocyte Ratio with Nutrition in Patients with Various Types of Malignant Tumors: A Multicenter Cross-Sectional Study.

Aim: Neutrophil-to-lymphocyte ratio (NLR) is an index of systemic inflammation. This study is to clarify the role of NLR in body functional status, nutritional risk and nutritional status in the course of tumor. Methods: A multi-center cross-sectional study of patients with various types of malignant tumors was accrued from the whole country. There were 21,457 patients with completed clinical data, biochemical indicators, physical examination, the Patient-Generated Subjective Global Assessment (PG-SGA) and Nutrition Risk Screening 2002 (NRS2002) survey. Logistic regression analysis was used to figure out the influencing factors of NLR, and four models were established to evaluate the influence of NLR on body functions, nutritional risks and nutritional status. Results: Male patients, TNM stage IV, total bilirubin, hypertension and coronary atherosclerotic heart disease (CAHD) were independent predictors of NLR >2.5. BMI, digestive systemic tumors and triglyceride negatively affect NLR in multivariable logistic regression. NLR was an independent predictor of Karnofsky Performance Scale (KPS), fat store deficit in all degrees, moderate and severe muscle deficit, mild fluid retention and PG-SGA grade. Conclusion: Male patients and those with hypertension and CAHD are prone to systemic inflammation. Systemic inflammation significantly degrades body function status and nutritional status, increases nutritional risk and influences fat and muscle metabolism in patients with malignant tumor. Improving the intervenable indicators such as elevating albumin and pre-albumin, decreasing total bilirubin and enhancing nutrition support are imperative. Obesity and triglyceride behave like anti-systemic inflammation, which is misleading due to reverse causation in the course of malignancy.

Construction of a Machine Learning Model to Estimate Physiological Variables of Speed Skating Athletes Under Hypoxic Training Conditions.

ABSTRACT: Han, J, Liu, M, Shi, J, and Li, Y. Construction of a machine learning model to estimate physiological variables of speed skating athletes under hypoxic training conditions. J Strength Cond Res 37(7): 1543-1550, 2023-Monitoring changes in athletes' physiological variables is essential to create a safe and effective hypoxic training plan for speed skating athletes. This research aims to develop a machine learning estimation model to estimate physiological variables of athletes under hypoxic training conditions based on their physiological measurements collected at sea level. The research team recruited 64 professional speed skating athletes to participate in a 10-week training program, including 3 weeks of sea-level training, followed by 4 weeks of hypoxic training and then a 3-week sea-level recovery period. We measured several physiological variables that could reflect the athletes' oxygen transport capacity in the first 7 weeks, including red blood cell (RBC) count and hemoglobin (Hb) concentration. The physiological variables were measured once a week and then modeled as a mathematical model to estimate measurements' changes using the maximum likelihood method. The mathematical model was then used to construct a machine learning model. Furthermore, the original data (measured once per week) were used to construct a polynomial model using curve fitting. We calculated and compared the mean absolute error between estimated values of the 2 models and measured values. Our results show that the machine learning model estimated RBC count and Hb concentration accurately. The errors of the estimated values were within 5% of the measured values. Compared with the curve fitting polynomial model, the accuracy of the machine learning model in estimating hypoxic training's physiological variables is higher. This study successfully constructed a machine learning model that used physiological variables measured at the sea level to estimate the physiological variables during hypoxic training.

Coordination Polymer Electrocatalysts Enable Efficient CO-to-Acetate Conversion.

Upgrading carbon dioxide/monoxide to multi-carbon C2+ products using renewable electricity offers one route to more sustainable fuel and chemical production. One of the most appealing products is acetate, the profitable electrosynthesis of which demands a catalyst with higher efficiency. Here, a coordination polymer (CP) catalyst is reported that consists of Cu(I) and benzimidazole units linked via Cu(I)-imidazole coordination bonds, which enables selective reduction of CO to acetate with a 61% Faradaic efficiency at -0.59 volts versus the reversible hydrogen electrode at a current density of 400 mA cm-2 in flow cells. The catalyst is integrated in a cation exchange membrane-based membrane electrode assembly that enables stable acetate electrosynthesis for 190 h, while achieving direct collection of concentrated acetate (3.3 molar) from the cathodic liquid stream, an average single-pass utilization of 50% toward CO-to-acetate conversion, and an average acetate full-cell energy efficiency of 15% at a current density of 250 mA cm-2 .