Association of alcohol consumption with hypertension or prehypertension in Chinese adolescent: A cohort study of the China Health and Nutrition Survey.

This study was conducted to investigate the association between alcohol consumption in adolescence and the risk of hypertension or prehypertension development in early adulthood. This cohort study included adolescent participants aged 12-18 years from the 2000-2011 China Health and Nutrition Survey. Cox proportional risk regression models were used to analyze the associations of the frequency of alcohol consumption, alcohol intake, and type of alcohol with the risk of developing hypertension or prehypertension. Restricted cubic spline analysis was used to assess the dose-response relationships for alcohol intake and their hazard ratios (HRs). A total of 1556 participants were included in the final analysis. Among the overall population, 448 (30.81%) and 35 (34.31%) participants developed hypertension or prehypertension, respectively. Compared with no alcohol consumption, alcohol consumption ≥ 2 times/week and consumption of ≥2 types of alcohol were associated with an increased risk of hypertension and prehypertension, with HRs of 1.97 (95% confidence interval [CI] 1.17-3.34; p = 0.011) and 1.77 (95% CI 1.01-3.09; p = 0.046), respectively. Alcohol intake of > 96 mL/week was associated with an increased risk of hypertension and prehypertension, with HRs of 2.09 (95% CI 1.12-3.90; p = 0.020) and 2.07 (95% CI 1.11-3.84; p = 0.021), respectively. The restricted cubic spline analysis showed that the risk of developing high blood pressure or prehypertension tends to increase with increasing alcohol consumption. Heavy alcohol consumption in adolescence increased the risk of developing hypertension and prehypertension in early adulthood.

Proangiogenic potential of plasma exosomes from prostate cancer patients.

Angiogenesis plays a pivotal role in the progression and metastasis of solid cancers, including prostate cancer (PCa). While small extracellular vesicles derived from PCa cell lines induce a proangiogenic phenotype in vascular endothelial cells, the contribution of plasma exosomes from patients with PCa to this process remains unclear. Here, we successfully extracted and characterized plasma exosomes. Notably, a ring of PKH67-labeled exosomes was observed around the HUVEC nucleus using fluorescence microscopy, indicating the uptake of exosomes by HUVEC. At the cellular level, PCa plasma exosomes enhanced angiogenesis, proliferation, invasion, and migration of HUVEC cells. Moreover, PCa plasma exosomes promoted angiogenesis and aortic sprouting. MicroRNAs are the most common genetic material in exosomes, and to identify miRNAs associated with the angiogenic response, we performed small RNA sequencing followed by RT-qPCR and bioinformatics analysis. These analyses revealed distinct miRNA profiles in plasma exosomes from patients with PCa compared to healthy individuals. Notably, hsa-miR-184 emerged as a potential regulator implicated in the proangiogenic effects of PCa plasma exosomes.

Structural adaptability and surface activity of peptides derived from tardigrade proteins.

Tardigrades are unique micro-organisms with a high tolerance to desiccation. The protection of their cells against desiccation involves tardigrade-specific proteins, which include the so-called cytoplasmic abundant heat soluble (CAHS) proteins. As a first step towards the design of peptides capable of mimicking the cytoprotective properties of CAHS proteins, we have synthesized several model peptides with sequences selected from conserved CAHS motifs and investigated to what extent they exhibit the desiccation-induced structural changes of the full-length proteins. Using circular dichroism spectroscopy, two-dimensional infrared spectroscopy, and molecular dynamics simulations, we have found that the CAHS model peptides are mostly disordered, but adopt a more α $$ \alpha $$ -helical structure upon addition of 2,2,2-trifluoroethanol, which mimics desiccation. This structural behavior is similar to that of full-length CAHS proteins, which also adopt more ordered conformations upon desiccation. We also have investigated the surface activity of the peptides at the air/water interface, which also mimics partial desiccation. Interestingly, sum-frequency generation spectroscopy shows that all model peptides are surface active and adopt a helical structure at the air/water interface. Our results suggest that amino acids with high helix-forming propensities might contribute to the propensity of these peptides to adopt a helical structure when fully or partially dehydrated. Thus, the selected sequences retain part of the CAHS structural behavior upon desiccation, and might be used as a basis for the design of new synthetic peptide-based cryoprotective materials.

The Impact of Hydrogen Sulfide in the Paraventricular Nucleus on the MAPK Pathway in High Salt-Induced Hypertension.

The hypothalamic paraventricular nucleus (PVN) plays a central role in regulating cardiovascular activity and blood pressure (BP). We administered hydroxylamine hydrochloride (HA), a cystathionine-ß-synthase (CBS) inhibitor, into the PVN to suppress endogenous hydrogen sulfide (H2S) and investigate its effects on the mitogen-activated protein kinase (MAPK) pathway in high salt-induced hypertension. We randomly divided 40 male Dahl salt-sensitive rats into 4 groups: the NS+PVN vehicle group, the NS+PVN HA group, the HS+PVN vehicle group, and the HS+PVN HA group, with 10 rats in each group. The rats in the NS (normal salt) groups were fed a normal-salt diet containing 0.3% NaCl, while the HS (high salt) groups were fed a high-salt diet containing 8% NaCl. The mean arterial pressure (MAP) was calculated after noninvasive measurement using an automatic sphygmomanometer to occlude the tail cuff once a week. HA or vehicle was infused into the bilateral PVN using Alzet osmotic mini-pumps for 6 weeks after the hypertension model was successfully established. We measured the levels of H2S in the PVN and plasma norepinephrine (NE) using ELISA. Additionally, we assessed the parameters of the MAPK pathway, inflammation, and oxidative stress through western blotting, immunohistochemical analysis, or real-time PCR. In the current study, we discovered that decreased levels of endogenous hydrogen sulfide in the PVN contributed to the onset of high salt-induced hypertension. This was linked to the activation of the MAPK signaling pathway, proinflammatory cytokines, and oxidative stress in the PVN, as well as the activation of the sympathetic nervous system.

Integrating plasma exosomal miRNAs, ultrasound radiomics and tPSA for the diagnosis and prediction of early prostate cancer: a multi-center study.

INTRODUCTION: This multi-center study aims to explore the roles of plasma exosomal microRNAs (miRNAs), ultrasound (US) radiomics, and total prostate-specific antigen (tPSA) levels in early prostate cancer detection. METHODS: We analyzed the publicly available dataset GSE112264 to identify the differentially expressed miRNAs associated with prostate cancer. Then, PyRadiomics was used to extract image features, and least absolute shrinkage and selection operator (LASSO) was used to screen the data. Subsequently, according to strict inclusion and exclusion criteria, the internal dataset (n = 199) was used to construct a diagnostic model, and the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and DeLong test were used to evaluate its diagnostic performance. Finally, we used an external dataset (n = 158) for further validation. RESULTS: The number of features extracted by PyRadiomics was 851, and the number of features screened by LASSO was 23. We combined the hsa-miR-320c, hsa-miR-944, radiomics, and tPSA features to construct a joint model. The area under the ROC curve of the combined model was 0.935. In the internal validation, the area under the curve (AUC) of the training set was 0.943, and the AUC of the test set was 0.946. The AUC of the external data set was 0.910. The calibration curve and decision curve were consistent with the performance of the combined model. There was a significant difference in the prediction ability between the combined prediction model and the single index prediction model, indicating the high credibility and accuracy of the combined model in predicting PCa. CONCLUSIONS: The combined prediction model, consisting of plasma exosomal miRNAs (hsa-miR-320c and hsa-miR-944), US radiomics, and clinical tPSA, can be utilized for the early diagnosis of prostate cancer.

On Inhomogeneous Infinite Products of Stochastic Matrices and Their Applications.

With the growth of the magnitude of multiagent networks, distributed optimization holds considerable significance within complex systems. Convergence, a pivotal goal in this domain, is contingent upon the analysis of infinite products of stochastic matrices (IPSMs). In this work, the convergence properties of inhomogeneous IPSMs are investigated. The convergence rate of inhomogeneous IPSMs toward an absolute probability sequence π is derived. We also show that the convergence rate is nearly exponential, which coincides with existing results on ergodic chains. The methodology employed relies on delineating the interrelations among Sarymsakov matrices, scrambling matrices, and positive-column matrices. Based on the theoretical results on inhomogeneous IPSMs, we propose a decentralized projected subgradient method for time-varying multiagent systems with graph-related stretches in (sub)gradient descent directions. The convergence of the proposed method is established for convex objective functions and extended to nonconvex objectives that satisfy Polyak-Lojasiewicz (PL) conditions. To corroborate the theoretical findings, we conduct numerical simulations, aligning the outcomes with the established theoretical framework.

Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer.

The potential of serum extracellular vesicles (EVs) as non-invasive biomarkers for diagnosing colorectal cancer (CRC) remains elusive. We employed an in-depth 4D-DIA proteomics and machine learning (ML) pipeline to identify key proteins, PF4 and AACT, for CRC diagnosis in serum EV samples from a discovery cohort of 37 cases. PF4 and AACT outperform traditional biomarkers, CEA and CA19-9, detected by ELISA in 912 individuals. Furthermore, we developed an EV-related random forest (RF) model with the highest diagnostic efficiency, achieving AUC values of 0.960 and 0.963 in the train and test sets, respectively. Notably, this model demonstrated reliable diagnostic performance for early-stage CRC and distinguishing CRC from benign colorectal diseases. Additionally, multi-omics approaches were employed to predict the functions and potential sources of serum EV-derived proteins. Collectively, our study identified the crucial proteomic signatures in serum EVs and established a promising EV-related RF model for CRC diagnosis in the clinic.

Biochar alleviates inhibition effects of humic acid on anaerobic digestion: Insights to performances and mechanisms.

To recover methane from waste activated sludge through anaerobic digestion (AD) is one promising alternative to achieve carbon neutrality for wastewater treatment plants. However, humic acids (HAs) are one of the major compositions in waste activated sludge, and their accumulation performs inhibition effects on AD. This study investigated the potentials of biochar (BC) in alleviating inhibition effects of HAs on AD. Results showed that although the accumulated HAs reduced methane yield by 9.37% compared to control, the highest methane yield, 132.6 mL CH4/g VSS, was obtained after adding BC, which was 45.9% higher than that in HA group. Mechanism analysis showed that BC promoted the activities of hydrolase such as protease and α-glucosidase, which were 69.7% and 29.7% higher than those in HA group, respectively. The conversion of short-chain fatty acids was accelerated. In addition, the evolutions of electroactive microorganisms like Clostridium_sensu_stricto_13 and Methanosaeta were consistent with the activitiies of electron transfer and the contents of cytochrome c. Furthermore, parts of HAs rather than all of them were adsorbed by BC, and the remaining free HAs and BC formed synergistic effects on methanogenesis, then both CO2 reduction and acetoclastic methanogenesis pathways were improved. The findings may provide some solutions to alleviate inhibition effects of HAs on AD.

Roles of quorum-sensing molecules in methane production from anaerobic digestion aided by biochar.

Biochar has been used to enhance methane generation from anaerobic digestion through establishing direct interspecific electron transfer between microorganisms. However, the microbial communication is still inadequate, thereby limiting further methane production improvement contributed by biochar. This study investigated the roles of quorum-sensing molecules, acylated homoserine lactone (AHL), in anaerobic digestion of waste activated sludge aided by biochar. Results showed that the co-addition of separated biochar and AHL achieved best methane production performance, with the maximal methane yield of 154.7 mL/g volatile suspended solids, which increased by 51.9%, 47.2%, 17.9%, and 39.4% respectively compared to that of control, AHL-loaded biochar, sole AHL, and sole biochar groups. The reason was that the co-addition of separated biochar and AHL promoted the stages of hydrolysis and acidification, promoting the conversion of organic matters and short-chain fatty acids, and optimizing the accumulation of acetate acid. Moreover, the methanogenesis stage also performed best among experimental groups. Correspondingly, the highest activities of electron transfer and coenzyme F420 were obtained, with increase ratios of 33.2% and 27.2% respectively compared to that of control. Furthermore, biochar did more significant effects on the evolution of microbial communities than AHL, and the direct interspecific electron transfer between fermentative bacteria and methanogens were possibly promoted.

Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

Background: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection. Method: We collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations. Results: In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively. Conclusion: We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.

Relationship evolution shapes inter-brain synchrony in affective sharing: The role of self-expansion.

The development of social relationships influences a person's self-concept, which in turn affects their perceptions and neural correlates in social interactions. This study employed an EEG-based hyperscanning technique and a longitudinal design to investigate how the evolution of interpersonal relationships impacts inter-brain synchrony during nonverbal social-emotional interactions. The framework for this study is based on the self-expansion model. We found that dyads exhibited enhanced affective sharing abilities and increased brain-to-brain synchrony, particularly in the gamma rhythm across the frontal, parietal, and left temporoparietal regions, after seven months together compared to when they first met. Additionally, the results indicate that inter-brain coupling evolves as relationships develop, with synchrony in nonverbal social-emotional interactions increasing as self-expansion progresses. Crucially, in the deep learning model, interpersonal closeness can be successfully classified by inter-brain synchrony during emotional-social interactions. The longitudinal EEG-hyperscanning design of our study allows for capturing dynamic changes over time, offering new insights into the neurobiological foundations of social interaction and the potential of neural synchrony as a biomarker for relationship dynamics.

Genetic Analysis of Stripe Rust Resistance in the Chinese Wheat Cultivar Luomai 163.

Stripe or yellow rust (YR) caused by Puccinia striiformis tritici (Pst) is an important foliar disease affecting wheat production globally. Resistant varieties are the most economically and environmentally effective way to manage this disease. The common winter wheat (Triticum aestivum L.) cultivar Luomai 163 exhibited resistance to Pst races CYR32 and CYR33 at the seedling stage and showed a high level adult plant resistance in the field. To understand the genetic basis of YR resistance in this cultivar, 142 F5 recombinant inbred lines (RILs) derived from cross Apav#1 × LM163 and both parents were genotyped with the 16K SNP array and bulked segregant analysis sequencing (BSA-Seq). The analysis detected a major gene, YrLM163, at the seedling stage associated with the 1BL.1RS translocation. Additionally, three genes for resistance at the adult plant stage were detected on chromosome arms 1BL (Lr46/Yr29/Pm39/Sr58), 6BS and 6BL in Luomai 163, whereas Apav#1 contributed resistance at a QTL on 2BL. These QTL explained YR disease severity variations ranging from 6.9 to 54.8%. KASP markers KASP-2BL, KASP-6BS and KASP-6BL for three novel loci QYr.hzau-2BL, QYr.hzau-6BS and QYr.hzau-6BL were developed and validated. QYr.hzau-1BL, QYr.hzau-2BL and QYr.hzau-6BS showed varying degrees of resistance to YR when present individually or in combination based on genotype and phenotype analysis of a panel of 570 wheat accessions. Six RILs combining resistance alleles of all QTL, showing higher resistance to YR in the field than Luomai 163 with disease severities of 10.7-16.0%, are important germplasm resources for breeding programs to develop YR resistant wheat varieties with good agronomic traits.

Continuously tunable separation of light-induced Haematococcus pluvialis using an ultrastretchable, sheath-flow-assisted elasto-inertial microchannel.

BACKGROUND: A proportion of Haematococcus pluvialis under the light stress can effectively conduct astaxanthin biosynthesis, leading to the increase in cell size. Although the size is a critical indicator for identifying the astaxanthin-rich H. pluvialis cells, the cut-off size to be separated varies from sample to sample. RESULTS: Here, we report an ultrastretchable, straight elasto-inertial microchannel with tunable separation threshold to continuously separate the light-induced H. pluvialis cells by size. The symmetrical sheath flows confine the particles to the channel sidewalls, and large particles can cross the interface of viscoelastic fluids to the equilibrium position at the channel centerline. By stretching the microfluidic chip, the medium-sized particles can gradually migrate to the channel centerline in the narrower and longer channel, bringing the tunable separation threshold. Results show that the separation performance of the ultrastretchable microfluidic device is affected by total flow rate, flow rate ratio of sheath to sample, polyethylene oxide (PEO) solution configuration. Lastly, size-tunable separation of light-induced H. pluvialis cells is demonstrated. SIGNIFICANCE: To the best of our knowledge, this is the first report on cell migration in co-flow configurations in the ultra-stretchable microfluidics. Separation of H. pluvialis is not only a relevant end application in harvesting the astaxanthin-rich species, but the separated populations of highly productive microalgal cells will open a venue for cellular directed evolution.

Assessing breast disease with deep learning model using bimodal bi-view ultrasound images and clinical information.

Breast cancer is the second leading cause of carcinoma-linked death in women. We developed a multi-modal deep-learning model (BreNet) to differentiate breast cancer from benign lesions. BreNet was constructed and trained on 10,108 images from one center and tested on 3,762 images from two centers in three steps. The diagnostic ability of BreNet was first compared with that of six radiologists; a BreNet-aided scheme was constructed to improve the diagnostic ability of the radiologists; and the diagnosis of real-world radiologists' scheme was then compared with the BreNet-aided scheme. The diagnostic performance of BreNet was superior to that of the radiologists (area under the curve [AUC]: 0.996 vs. 0.841). BreNet-aided scheme increased the pooled AUC of the radiologists from 0.841 to 0.934 for reviewing images, and from 0.892 to 0.934 in the real-world test. The use of BreNet significantly enhances the diagnostic ability of radiologists in the detection of breast cancer.

High-n Phase Suppression for Efficient and Stable Blue Perovskite Light-Emitting Diodes.

Quasi-2D perovskites light-emitting diodes (PeLEDs) have achieved significant progress due to their superior optical and electronic properties. However, the blue PeLEDs still exist inefficient energy transfer and electroluminescence performance caused by mixed multidimensional phase distribution. In this work, transition metal salt (zinc bromide, ZnBr2) is introduced to modulate phase distributions by suppressing the nucleation of high n phase perovskites, which effectively shortens the energy transfer path for blue emission. Moreover, ZnBr2 also facilitates energy level matching and reduces non-radiative recombination, thus improving electroluminescence (EL) efficiency. Benefiting from these combined improvements, an efficient blue PeLEDs is obtained with a maximum external quantum efficiency (EQE) of 16.2% peaking located at 486 nm. This work provides a promising approach to tune phase distribution of quasi-2D perovskites and achieving highly efficient blue PeLEDs.

Alteration of soil pH induced by submerging/drainage and application of peanut straw biochar and its impact on Cd(II) availability in an acidic soil to indica-japonica rice varieties.

The effects of soil pH variations induced by submergence/drainage and biochar application on soil cadmium (Cd) availability to different rice (Oryza sativa L.) varieties are not well understood. This study aims to investigate the possible reasons for available Cd(II) reduction in paddy soil as influenced by biochar and to determine Cd(II) absorption and translocation rates in different parts of various rice varieties. A pot experiment in a greenhouse using four japonica and four indica rice varieties was conducted in Cd(II) contaminated paddy soil with peanut straw biochar. The results indicated that the submerging led to an increase in soil pH due to the consumption of protons (H+) by the reduction reactions of iron/manganese (Fe/Mn) oxides and sulfate (SO42-) and thus the decrease in soil available Cd(II) contents. However, the drainage decreased soil pH due to the release of protons during the oxidation of Fe2+, Mn2+, and S2- and thus the increase in soil available Cd(II) contents. Application of the biochar increased soil pH during soil submerging and inhibited the decline in soil pH during soil drainage, and thus decreased soil available Cd(II) contents under both submerging and drainage conditions. The indica rice varieties absorbed more Cd(II) in their roots and accumulated higher amounts of Cd(II) in their shoots and grains than the japonica rice varieties. The Cd(II) sensitive varieties exhibited a greater absorption and translocation rate of Cd(II) compared to the tolerant varieties of both indica and japonica rice. Biochar inhibited the absorption and accumulation of Cd(II) in the rice varieties, which ultimately lowered the Cd(II) contents in rice grains below the national food safety limit (0.2 mg kg-1). Overall, planting japonica rice varieties in Cd(II) polluted paddy soils combined with the use of biochar can effectively reduce Cd(II) content in rice grains which protects human health against Cd(II) toxicity.

Implicit 3D Human Reconstruction Guided by Parametric Models and Normal Maps.

Accurate and robust 3D human modeling from a single image presents significant challenges. Existing methods have shown potential, but they often fail to generate reconstructions that match the level of detail in the input image. These methods particularly struggle with loose clothing. They typically employ parameterized human models to constrain the reconstruction process, ensuring the results do not deviate too far from the model and produce anomalies. However, this also limits the recovery of loose clothing. To address this issue, we propose an end-to-end method called IHRPN for reconstructing clothed humans from a single 2D human image. This method includes a feature extraction module for semantic extraction of image features. We propose an image semantic feature extraction aimed at achieving pixel model space consistency and enhancing the robustness of loose clothing. We extract features from the input image to infer and recover the SMPL-X mesh, and then combine it with a normal map to guide the implicit function to reconstruct the complete clothed human. Unlike traditional methods, we use local features for implicit surface regression. Our experimental results show that our IHRPN method performs excellently on the CAPE and AGORA datasets, achieving good performance, and the reconstruction of loose clothing is noticeably more accurate and robust.

Multi-walled carbon nanotubes affect yield, antioxidant response, and rhizosphere microbial community of scented rice under combined cadmium-lead (Cd-Pb) stress.

Rice production is threatened by heavy metal stress. The use of multi-walled carbon nanotubes (MWCNTs) in agriculture has been reported in previous studies. We aimed to quantify the impact of MWCNTs on the growth and physiological characteristics of scented rice under cadmium (Cd) and lead (Pb) stresses. Therefore, a pot experiment was conducted, two scented rice varieties Yuxiangyouzhan and Xiangyaxiangzhan were used as materials grown under different concentrations of MWCNTs (0, 100, and 300 mg kg-1 recorded as CK, CNPs100, and CNPs300, respectively). The yield, antioxidant response, and rhizosphere microbial community of scented rice were studied. The results showed that compared with the CK treatment, the CNPs100 and CNPs300 treatments increased leaf dry weight by 17.95%-56.22% at the heading stage, and the H2O2 content in leaves decreased significantly by 36.64%-42.27% at the maturity stage. Under CNPs100 treatment, the grain yield of two scented rice varieties increased significantly by 17.54% and 27.40%, respectively. The MWCNTs regulated the distribution of the Cd and Pb in different plant tissues. The content of Cd (0.11-0.20 mg kg-1) and Pb (0.01-0.04 mg kg-1) in grain were at a safety level (<0.2 mg kg-1). Moreover, MWCNTs increased soil microbial community abundance and altered community composition structure under Cd-Pb stress, which in turn improved agronomic traits and quality of scented rice. Overall, this study suggested that the application of MWCNTs regulates the growth, yield, physiological response, and soil microbial community, the genotypes response effect of scented rice to MWCNTs is needed further studied.

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU). STUDY DESIGN: This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis. RESULTS: A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96. CONCLUSIONS: The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk.

PCDH11X mutation as a potential biomarker for immune checkpoint therapies in lung adenocarcinoma.

Immune checkpoint inhibitors (ICIs) have achieved impressive success in lung adenocarcinoma (LUAD). However, the response to ICIs varies among patients, and predictive biomarkers are urgently needed. PCDH11X is frequently mutated in LUAD, while its role in ICI treatment is unclear. In this study, we curated genomic and clinical data of 151 LUAD patients receiving ICIs from three independent cohorts. Relations between PCDH11X and treatment outcomes of ICIs were examined. A melanoma cohort collected from five published studies, a pan-cancer cohort, and non-ICI-treated TCGA-LUAD cohort were also examined to investigate whether PCDH11X mutation is a specific predictive biomarker for LUAD ICI treatment. Among the three ICI-treated LUAD cohorts, PCDH11X mutation (PCDH11X-MUT) was associated with better clinical response compared to wild-type PCDH11X (PCDH11X-WT). While in ICI-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and the non-ICI-treated TCGA-LUAD cohort, no significant differences in overall survival (OS) were observed between the PCDH11X-MUT and PCDH11X-WT groups. PCDH11X mutation was associated with increased PD-L1 expression, tumor mutation burden (TMB), neoantigen load, DNA damage repair (DDR) mutations, and hot tumor microenvironment in TCGA-LUAD cohort. Our findings suggested that the PCDH11X mutation might serve as a specific biomarker to predict the efficacy of ICIs for LUAD patients. Considering the relatively small sample size of ICI-treated cohorts, future research with larger cohorts and prospective clinical trials will be essential for validating and further exploring the role of PCDH11X mutation in the context of immunotherapy outcomes in LUAD. KEY MESSAGES: PCDH11X mutation is associated with better clinical response compared to wild type PCDH11X in three ICIs-treated LUAD cohorts. In ICIs-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and non-ICIs-treated TCGA-LUAD cohorts PCDH11X mutation is not associated with better clinical response, suggesting PCDH11X mutation might be a specific biomarker to predict the efficacy of ICIs treatment for LUAD patients. PCDH11X mutation is associated with increased PD-L1 expression, tumor mutation burden, and neoantigen load in TCGA-LUAD cohort. PCDH11X mutation is associated with hot tumor microenvironment in TCGA-LUAD cohort.