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Abstract This study aimed to identify factors associated with optimal weight loss response by analyzing pre-weight loss data from a cohort of 2577 patients with obesity who visited weight management clinics between 2013 and 2022. Out of these, 1276 patients had follow-up data available. Following dietary and exercise interventions, 580 participants achieved optimal weight loss outcomes. Participants were subsequently divided into two groups based on their weight loss outcomes: those who achieved optimal weight loss response and those who did not. Statistical analysis, conducted using RStudio, identified thirteen predictor variables through LASSO and logistic regression, with age emerging as the most influential predictor. A nomogram was developed to predict optimal weight loss response, showing good predictive performance (AUC = 0.807) and clinical applicability, validated by internal validation methods. Decision curve analysis (DCA) further illustrated the nomogram's clinical utility. The developed nomogram prediction model for optimal weight loss response is user-friendly, highly accurate, and demonstrates excellent discriminative and calibration capabilities.

Objective: To create a nomogram-based model to estimate the Chinese population's 5-year risk of metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: We randomly divided 7582 participants into two groups in a 7:3 ratio: one group was assigned to work with the training set, which consisted of 5307 cases, and the other group was assigned to validate the model using 2275 cases. The least absolute shrinkage and selection operator model was employed to ascertain the variables with the highest correlation among all potential variables. A logistic model was constructed by incorporating these selected variables, which were subsequently visualized using a nomogram. The discriminatory ability, calibration, and clinical utility of the model were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: During the 5-year follow-up, 1034 (13.64%) total participants were newly diagnosed with MASLD. Using eight variables (gender, body mass index, waist, hemoglobin, alanine aminotransferase, uric acid, triglycerides, and high-density lipoprotein), we built a 5-year MASLD risk prediction model. The nomogram showed an area under the ROC of 0.795 (95% CI: 0.779–0.811) in the training set and 0.785 (95% CI: 0.760–0.810) in the validation set. The calibration curves revealed a 5-year period of agreement between the observed and predicted MASLD risks. DCA curves illustrated the practicality of this nomogram over threshold probability profiles ranging from 5% to 50%. Conclusion: We created and tested a nomogram to forecast the risk of MASLD prevalence over the next 5 years.

In today's modern world, marketing is a fruitful source to grow a business by reaching a larger audience. This paper considers a popular social media source called LinkedIn.com as a way of marketing or reaching a larger audience. We consider a regression approach to explore the statistically significant role of the LinkedIn medium as a tool for reaching a larger audience. In addition to statistically exploring the role of the LinkedIn medium in the financial sector, we also introduce a new Weibull-based probability distribution, namely, the exponential TX inverse Weibull (for short'ETXI-Weibull') distribution. The ETXI-Weibull distribution is specially designed for analyzing LinkedIn marketing data. The beauty of the ETXI-Weibull distribution is that it satisfies the heavy-tailed characteristics, which are explored visually and mathematically. For the ETXI-Weibull distribution, the estimators are derived whose performances are evaluated visually and numerically through a simulation study. The ETXI-Weibull distribution is applied practically by considering the LinkedIn marketing data set as an example. Using certain established statistical tests, we observe that the ETXI-Weibull distribution provides an optimal fit to the LinkedIn marketing data set. Furthermore, a bivariate extension of the exponential TX inverse Weibull model is also introduced. Finally, the bivariate extension of the exponential TX inverse Weibull model is illustrated by modeling the LinkedIn marketing and sales data.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is easily neglected in the non-obese population. TyG index (triglyceride glucose Index) and TG/HDL-c (triglyceride to high-density lipoprotein cholesterol) are new indicators to evaluate insulin resistance (IR). Fibroscan is a non-invasive way to assess hepatic steatosis [by control attenuation parameters (CAP)] and fibrosis [by liver stiffness measurement (LSM)].The purpose of this study was to explore the correlation of TyG and its combination with obesity indicators [TyG-waist circumference (WC), TyG-body mass index (BMI)] and TG/HDL-c with CAP and LSM. Method One thousand seven hundred seventy-six adults (age ≥ 20 years, BMI

Abstract Background Aortic dissection (AD) is a rare disease with high mortality for which no effective diagnostic biomarkers are available. Human cytomegalovirus (HCMV) infection is an important cause of the occurrence and progression of many diseases, but the relationship between HCMV infection and AD is not clear. Methods In this study, we first used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to determine the expression profile of 25 HCMV-encoded microRNAs (HCMV miRNAs) in the plasma within a training set consisting of 20 AD patients and 20 healthy controls. Then, abnormal expressed HCMV miRNAs were verified in a validation set of 12 AD patients and 12 healthy controls. In addition, HCMV infection was detected in the third cohort consisting of 20 AD patients and 20 healthy controls. Results The 95% quantile of the expression levels of HCMV miRNAs in the training set was used as the threshold for distinction between AD patients and healthy controls. The proportion of individuals with high level of five types of HCMV miRNAs was significantly different between AD patients and healthy controls. In the validation set, only the proportion of individuals with high levels of hcmv-miR-UL112-5p and hcmv-miR-UL22A-5p, two of the five HCMV miRNAs obtained in the preliminary screening, showed significant difference between AD patients and healthy controls. In the third cohort, there was no significant difference in HCMV DNA levels and anti-HCMV IgG concentrations between AD patients and healthy controls. Conclusions The HCMV miRNAs levels in plasma differed in AD patients and healthy controls. This finding may contribute to a further understanding of the relationship between HCMV infection and AD and are worthy of future research on the diagnosis and etiology of AD.

How reactive oxygen species regulate neuronal stem cell (NSC) behavior is poorly understood. Here, the authors report that Ncf1-dependent oxidation of Igfbp2 cystines represses DNA repair networks to regulate NSC self-renewal and cell fate decisions.

The purpose of this research is in-depth understanding of the internal causes of exergy destruction in various parts of the system and to identify potential improvements for the components. The focus is on a combined cycle power generation system that utilizes the organic Rankine cycle (ORC) and direct expansion cycle (DEC). To investigate the primary sources of exergy destruction in each component, advanced exergy analysis (AEA) is utilized. The result demonstrates that the net out power of the proposed system can reach 106.64 kW with energy efficiency of 11.22%, and exergy efficiency of 21.40%. The heat exchanger is identified as the primary contributor to exergy destruction, constituting 81.70% of the total ratio. Specifically, the condenser exhibits the highest exergy destruction ratio at 59.82%, indicating a need for prioritized optimization efforts. The findings of AEA reveal that the primary source of component irreversibility stems from the endogenous part. This shows that, while most exergy destruction is unavoidable, there remains room for system improvement. Regarding the turbine, its exergy destruction is primarily attributed to inefficiencies, leading to irreversibility. Nevertheless, there is exergy destruction that may be avoidable and can be reduced by 25.93 kW, which is 2.5 times greater than that of the heat exchanger. This finding underscores the high potential for improvement in ORC and DEC turbines, making them a priority for optimization efforts.

With the rise of the Internet of Things (IoT), maintaining data confidentiality and protecting user privacy have become increasingly challenging. End devices in the IoT are often deployed in unattended environments and connected to open networks, making them vulnerable to physical tampering and other security attacks. Different authentication key agreement (AKA) schemes have been used in practice; several of them do not cover the necessary security features or are incompatible with resource-constrained end devices. Their security proofs have been performed under the Random-Oracle model. We present an AKA protocol for end devices and servers. The proposal leverages the ECC-based key exchange mechanism and one-way hash function-based message authentication method to achieve mutual authentication, user anonymity, and forward security. A formal security proof of the proposed scheme is performed under the standard model and the eCK model with the elliptic curve encryption computational assumptions, and formal verification is performed with ProVerif. According to the performance comparison, it is revealed that the proposed scheme offers user anonymity, perfect forward security, and mutual authentication, and resists typical attacks such as ephemeral secret leakage attacks, impersonation attacks, man-in-the-middle attacks, and key compromise impersonation attacks. Moreover, the proposed scheme has the lowest computational and communication overhead compared to existing schemes.

Cadmium (Cd) is the most serious heavy metal pollutant in the agricultural soil of China and can transfer and accumulate through the food chain and affect the growth, development and physiological processes of phytophagous insect. Glyphodes pyloalis Walker (G. pyloalis) is one of the most important pests of mulberry, and there are few studies on the adverse effects of heavy metals on insects, especially mulberry pests. To understand the toxicology of Cd exposure on G. pyloalis, we investigated the effects of three different concentrations of Cd (0, 3.89 and 51.69 mg/kg, labeled as control check (CK), low dose (LD) and high dose (HD)) on the development and detoxification mechanism of G. pyloalis and explored the molecular mechanism of Cd on G. pyloalis larvae using RNA-seq technology. Transcriptome analysis showed that compared with the CK, a total of 63 differentially expressed genes (DEGs) were identified in LD exposure, including 24 upregulated and 39 downregulated candidates. In CK versus HD groups, 395 upregulated DEGs and 436 downregulated DEGs were identified and the expression patterns of 12 genes related to detoxification and metabolism were verified using qPCR. These DEGs were relevant to multiple specific peroxisome and drug metabolism-cytochrome P450 pathways. Gene annotation and quantitative real-time PCR revealed that a high concentration of Cd significantly stimulated the expression of metabolic detoxification enzyme genes. The results revealed that Cd exposure changed the pupal weight, adult emergence rate and the activities of AKP, ACP and GST in G. pyloalis and induced notable adverse effects at the molecular level on detoxification and metabolism such as concentration and time differences. In addition, we silenced CYP12A2 by RNA interference. Bioassays showed that after silencing CYP12A2, the survival rate of G. pyloalis under Cd exposure was lower than that of the control group.

Abstract Background Adiposity evaluated by body mass index (BMI) is associated with glycometabolism. The aim of the investigation was to explore the correlation of visceral fat area (VFA), body fat percentage (BFP), BMI and waist circumference (WC) with type 2 diabetes mellitus (T2DM) and pre-diabetes. Methods A total of 18,458 participates underwent physical examination in Nanjing Drum Tower Hospital from January 2018 to April 2022 was included in this study. Data were collected retrospectively. Regression analysis was used to evaluate the relationship of VFA, BFP, WC and BMI with diabetes status, fasting blood glucose (FBG) and glycohemoglobin (HbA1c). Results After fully adjusted for multiple covariates, VFA, BFP, WC and BMI in T2DM and pre-diabetes group exceeded compared with normal group. FBG was positively correlated with VFA, BFP, WC and BMI with βs of 2.221,0.306,0.606 and 0.175(p 7%), there might be a trend of fat loss. VFA could negatively affect glycometabolism independently from BMI. The optimum to evaluate the risk of glycometabolism disorder was WC.

Bacterial wilt is a significant soil-borne disease that poses a threat to mulberry production yield and quality of agricultural production worldwide. However, the disease resistance mechanisms dependent on root exudates are not well understood. In this present study, we investigated the antibacterial mechanisms of the main active substances (erucamide, oleamide, and camphor bromide) present in mulberry root exudates (MRE) against Ralstonia pseudosolanacearum (Rp), the causal agent of bacterial wilt. Our findings revealed that these three active substances inhibited the growth activity of Rp by affecting the cell morphology and extracellular polysaccharide content, as well as triggering a burst of reactive oxygen species. The active substances induced oxidative stress, leading to a decrease in Rp growth. Additionally, the expression levels of key genes in the hrp gene cluster (hrpB, hrpX, and hrpF) and other virulence-related genes (such as ripAW, ripAE, Rs5-4819, Rs5-4374, ace, egl3, and pehB) were significantly reduced upon treatment with the active substances. Further pathogenicity experiments demonstrated that root exudates (at a concentration of 1.5 mg·mL−1) delayed or slowed down the occurrence of bacterial wilt in mulberry. These findings provide valuable insight into the antimicrobial mechanisms of MRE against Rp and lay a theoretical foundation for the development and application of biocontrol agents to control mulberry bacterial wilt.

Permanent magnet synchronous motor (PMSM) servo system is the control and execution mechanism of spacecraft, and its precise modeling is of great practical significance for improving the performance of the spacecraft. A comprehensive model considering elasticity, backlash, and friction was established based on the structural composition and transmission characteristics of spacecraft. Firstly, an elastic-dead zone model was established based on the transmission characteristics, with two mass elastic and gear backlash dead zones as the main features. Then, an improved LuGre friction model was established that considers the effects of vibration and environmental temperature, and the three models were organically combined. Finally, a method for identifying nonlinear parameters of the system using sinusoidal sweep and the least squares method was proposed, and the accuracy of the constructed model was verified. The experiment shows that the error of the simulation results is consistent with the experimental results. This method can precisely obtain the servo system model, providing theoretical support for control method research.

Forecasting the amplitude and timing of the sunspot cycle is highly important for solar physics and space weather applications, but high-precision prediction of solar magnetic activity has remained an outstanding challenge. The Informer model, as the most advanced deep learning technique, is an ideal approach for predicting solar activity cycle. Using the whole-disk sunspot numbers (SSNs) between 1749 and 2023 and the hemispheric SSNs between 1992 and 2023, the amplitudes and timings of Solar Cycles 25 and 26 are predicted by the Informer model. The main results are the following: (1) the activity levels of Solar Cycles 25 and 26 continue being weak-moderate cycles with their strengths stronger than Solar Cycle 24, implying that the long-term solar variability is significantly modulated in length and magnitude by the Gleissberg century cycle; (2) the Gnevyshev peaks of Solar Cycles 25 and 26 are clearly observed with a higher value in the second peak, suggesting that the numbers of the large sunspot groups are greater compared to the small sunspot groups in these two cycles; and (3) during Solar Cycle 25, the activity level in the southern hemisphere is predicted to be stronger than that in the northern one, revealing significant asymmetry and asynchronization between the two hemispheres. Our analysis results show that solar cycle predictions can be made more accurate if performed separately for each hemisphere. Furthermore, Solar Cycles 25 and 26 are likely to be weak-moderate cycles, in agreement with the precursor-based and model-based prediction methods.

Objective The purposes of the study were to investigate the detailed association of serum uric acid (SUA) with visceral fat area (VFA) and body fat percentage (BFP) as calculated by bioelectrical impedance analysis (BIA) and build non-invasive diagnosis models of hyperuricaemia by combining obesity-related indicators, age and sex.Method A total of 19 343 adults were included. Multivariable regression analysis models were employed to analyse the association of SUA with VFA and BFP. Receiver operating characteristic curves were generated to diagnose hyperuricaemia in adults.Results After fully adjusting for covariates, SUA was positively associated with VFA, BFP and body mass index (BMI) with βs of 0.447, 2.522 and 4.630 (95% CI= (0.412 to 0.482), (2.321 to 2.723) and (4.266 to 4.994)). After stratification by gender, this association persists (p

Summary: STING is a well-known signaling adaptor essential for sensing cytosolic dsDNA to produce type I interferon. Although the detailed underlying mechanisms remain enigmatic, recent studies show that STING activation can lead to T lymphocyte apoptosis. Here, we report that AIFM1 facilitates STING activation-induced cell apoptosis in T lymphocytes. Mechanistically, AIFM1 is upregulated after STING activation in T cells but not in HEK293T-STING and THP-1 cells, rendering T cells more sensitive to apoptosis. In contrast to the canonical role of AIFM1 in the caspase-independent parthanatos, the function of AIFM1 is operated by the formation of an AIFM1/IRF3/BAX complex and mitochondrial outer membrane permeabilization, which cause cytochrome c release and caspase activation. Furthermore, supplementation with newly synthesized AIFM1 can reconstitute STING activation-induced cell apoptosis in HEK293T-STING and THP-1 cells. Our study identifies AIFM1 as a key regulating factor determining the hypersensitivity of T lymphocytes to STING activation-induced cell apoptosis.

BackgroundDecreasing mass and metabolism in skeletal muscle are associated with increasing insulin resistance (IR) and type 2 diabetes mellitus (T2DM). The causal relation between sarcopenia and abnormal glucose metabolism may be bidirectional. This investigation is aimed to explore the detailed correlation between pre-diabetes and sarcopenia in United States (US) adults.MethodsA total of 22,482 adults aged ≥20 years in the National Health and Nutrition Examination Survey (NHANES) were included. Generalized linear models were conducted to examine associations between diabetes status, serum glucose, glycohemoglobin (HbA1c), and sarcopenia. Generalized additive models and smooth fitting curves were used to examine the non-linear relationship between HbA1c and ASMBMI. Sarcopenia was defined as ASMBMI (appendicular skeletal muscle mass/body mass index) < 0.789 for males, and

Sulfolipid-1 (SL-1) is located in the Mycobacterium tuberculosis (M. tb) cell wall, and is essential for pathogen virulence and intracellular growth. Multiple proteins (e.g., Pks2, FadD23, PapA1, and MmpL8) in the SL-1 synthesis pathway can be treated as drug targets, but, to date, their structures have not been solved. The crystal structures of FadD23 bound to ATP or hexadecanoyl adenylate was determined in this study. We have also investigated long-chain saturated fatty acids as biological substrates of FadD23 through structural, biological, and chemical analyses. The mutation at the active site of FadD23 greatly influences enzymatic activity. Meanwhile, the FadD23 N-terminal domain alone cannot bind palmitic acid without C-terminal domain facilitation since it is almost inactive after removing the C-terminal domain. FadD23 is the first protein in the SL-1 synthesis pathway whose structure has been solved. These results reveal the importance of the C-terminal domain in the catalytic mechanism.

Abstract Background Waist circumference is becoming recognized as a useful predictor of health risks in clinical research. However, clinical datasets tend to lack this measurement and self-reported values tend to be inaccurate. Predicting waist circumference from standard physical features could be a viable method for generating this information when it is missing or mitigating the impact of inaccurate self-reports. This study determined the degree to which the XGBoost advanced machine learning algorithm could build models that predict waist circumference from height, weight, calculated Body Mass Index, age, race/ethnicity and sex, whether they perform better than current models based on linear regression, and the relative importance of each feature in this prediction. Methods We trained tree-based models (via XGBoost gradient boosting) and linear models (via regression) to predict waist circumference from height, weight, Body Mass Index, age, race/ethnicity and sex (n = 60,740 participants). We created 10 iterations of each model, each using 90% of the dataset for training and the remaining 10% for testing performance (this group was different for each iteration). We calculated model performance and feature importance as an average across 10 iterations. We then externally validated the ensembled version of the top model. Results The XGBoost model predicted waist circumference with a mean bias ± standard deviation of 0.0 ± 0.04 cm and a root mean squared error of 4.7 ± 0.05 cm, with performance varying slightly by sex and race/ethnicity. The XGBoost model showed varying degrees of improvement over linear regression models. The top 3 predictors were Body Mass Index, weight and race (Asian). External validation found that on average this model overestimated waist circumference by 4.65 cm in the United Kingdom population (mainly due to overprediction in females) and underestimated waist circumference by 1.7 cm in the Chinese population. The respective root mean squared errors were 7.7 cm and 7.1 cm. Conclusions XGBoost-based models accurately predict waist circumference from standard physical features. Waist circumference prediction using this approach would be valuable for epidemiological research and beyond.

Long non-coding RNAs (lncRNAs) are key regulators in the pathophysiology of gastric cancer, and lncRNAs have been regarded as potential biomarkers and therapeutic targets for gastric cancer. The present study performed the WGCNA analysis of the GSE70880 dataset and aimed to identify novel lncRNAs associated with gastric cancer progression. Based on the WGCNA, the lncRNAs and mRNA co-expression network were constructed. A total of four modules were identified and the eigengenes in different modules were involved in various key signaling pathways. Furthermore, the co-expression networks were constructed between the lncRNAs and mRNA; this leads to the identification of 6 modules, which participated in various cellular pathways. The survival analysis showed that high expression of CCDC144NL antisense RNA 1 (CCDC144NL-AS1) and LINC01614 was positively correlated with the poor prognosis of patients with gastric cancer. The in vitro validation results showed that CCDC144NL-AS1 and LINC01614 were both up-regulated in the gastric cancer cells. Silence of CCDC144NL-AS1 and LINC01614 both significantly suppressed the cell proliferation and migration of gastric cancer cells, and also promoted the chemosensitivity of gastric cancer cells to 5-fluorouracil. Collectively, our results suggested that the newly identified two lncRNAs (CCDC144NL-AS1 and LINC01614) may act as oncogenes in gastric cancer.

Trehalase is an essential hydrolase of trehalose in insects. However, whether and how trehalase performs in the association of parasitoid wasps and their hosts still remains unknown. Here, the exact function of trehalase of the general cutworm Spodoptera litura after it was parasitized by its predominant endoparasitoid Meterous pulchricornis was elucidated. Two trehalase genes (SlTre1, SlTre2) were identified, and they were highly expressed five days after parasitization by M. pulchricornis. Then, we successfully silenced SlTre1 and SlTre2 in parasitized third instar S. litura larvae. The content of glucose, which is the hydrolysate of trehalose, was significantly decreased after silencing SlTres in parasitized S. litura larvae, and the activities of trehalase were also notably reduced. In addition, the cocoon weight, the emergence rate, proportion of normal adults, and the body size of parasitoid offsprings were significantly decreased in SlTre1- or SlTre2-silenced groups compared to the controls. These results implied that parasitization by parasitoids regulated the trehalase of host larvae to create a suitable nutritional environment for the parasitoid offspring. The present study broadens the knowledge of trehalase in the interaction between parasitoids and their hosts and is of benefit to biological control of S. litura acting by parasitoid wasps.

ABSTRACT Protein phosphatase 2A (PP2A) accounts for the majority of total Ser/Thr phosphatase activities in most cell types and regulates many biological processes. PP2A holoenzymes contain a scaffold A subunit, a catalytic C subunit, and one of the regulatory/targeting B subunits. How the B subunit controls PP2A localization and substrate specificity, which is a crucial aspect of PP2A regulation, remains poorly understood. The kinetochore is a critical site for PP2A functioning, where PP2A orchestrates chromosome segregation through its interactions with BubR1. The PP2A-BubR1 interaction plays important roles in both spindle checkpoint silencing and stable microtubule-kinetochore attachment. Here we present the crystal structure of a PP2A B56-BubR1 complex, which demonstrates that a conserved BubR1 LxxIxE motif binds to the concave side of the B56 pseudo-HEAT repeats. The BubR1 motif binds to a groove formed between B56 HEAT repeats 3 and 4, which is quite distant from the B56 binding surface for PP2A catalytic C subunit and thus is unlikely to affect PP2A activity. In addition, the BubR1 binding site on B56 is far from the B56 binding site of shugoshin, another kinetochore PP2A-binding protein, and thus BubR1 and shugoshin can potentially interact with PP2A-B56 simultaneously. Our structural and biochemical analysis indicates that other proteins with the LxxIxE motif may also bind to the same PP2A B56 surface. Thus, our structure of the PP2A B56-BubR1 complex provides important insights into how the B56 subunit directs the recruitment of PP2A to specific targets.

The eddy current probe, which is flexible, array typed, highly sensitive and capable of quantitative inspection is one practical requirement in nondestructive testing and also a research hotspot. A novel flexible planar eddy current sensor array for the inspection of microcrack presentation in critical parts of airplanes is developed in this paper. Both exciting and sensing coils are etched on polyimide films using a flexible printed circuit board technique, thus conforming the sensor to complex geometric structures. In order to serve the needs of condition-based maintenance (CBM), the proposed sensor array is comprised of 64 elements. Its spatial resolution is only 0.8 mm, and it is not only sensitive to shallow microcracks, but also capable of sizing the length of fatigue cracks. The details and advantages of our sensor design are introduced. The working principal and the crack responses are analyzed by finite element simulation, with which a crack length sizing algorithm is proposed. Experiments based on standard specimens are implemented to verify the validity of our simulation and the efficiency of the crack length sizing algorithm. Experimental results show that the sensor array is sensitive to microcracks, and is capable of crack length sizing with an accuracy within ±0.2 mm.

Building information modelling (BIM) can be applied to tunnel engineering to address a number of problems, including complex structure, extensive design, long construction cycle and increased security risks. To promote the development of tunnel engineering in China, this paper combines actual cases, including the Xingu mountain tunnel and the Shigu Mountain tunnel, to systematically analyse BIM applications in tunnel engineering in China. The results indicate that BIM technology in tunnel engineering is currently mainly applied during the design stage rather than during construction and operation stages. The application of BIM technology in tunnel engineering covers many problems, such as a lack of standards, incompatibility of different software, disorganized management, complex combination with GIS (Geographic Information System), low utilization rate and poor awareness. In this study, through summary of related research results and engineering cases, suggestions are introduced and an outlook for the BIM application in tunnel engineering in China is presented, which provides guidance for design optimization, construction standards and later operation maintenance.