SARS-CoV-2 exacerbates proinflammatory responses in myeloid cells through C-type lectin receptors and Tweety family member 2.

Despite mounting evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) engagement with immune cells, most express little, if any, of the canonical receptor of SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2). Here, using a myeloid cell receptor-focused ectopic expression screen, we identified several C-type lectins (DC-SIGN, L-SIGN, LSECtin, ASGR1, and CLEC10A) and Tweety family member 2 (TTYH2) as glycan-dependent binding partners of the SARS-CoV-2 spike. Except for TTYH2, these molecules primarily interacted with spike via regions outside of the receptor-binding domain. Single-cell RNA sequencing analysis of pulmonary cells from individuals with coronavirus disease 2019 (COVID-19) indicated predominant expression of these molecules on myeloid cells. Although these receptors do not support active replication of SARS-CoV-2, their engagement with the virus induced robust proinflammatory responses in myeloid cells that correlated with COVID-19 severity. We also generated a bispecific anti-spike nanobody that not only blocked ACE2-mediated infection but also the myeloid receptor-mediated proinflammatory responses. Our findings suggest that SARS-CoV-2-myeloid receptor interactions promote immune hyperactivation, which represents potential targets for COVID-19 therapy.

Impact of the RNA allosteric effect triggered by single nucleotide polymorphisms on the splicing process.

The susceptibility single nucleotide polymorphisms (SNPs) obtained by genome-wide association studies leave some thorny questions, such as prioritization, false positives and unknown pathogenesis. Previous studies suggested that genetic variation may perturb the RNA secondary structure, influence protein recruitment and binding and ultimately affect splicing processes. Therefore, exploring the perturbation of SNPs to structure-function correlations may provide an effective bridge toward understanding the genetic contribution to diseases. Here, aiming to decipher the regulatory mechanism of myopia susceptibility variants, we systematically evaluated the roles of SNP-induced structural changes during splicing. In addition, 7.53% of myopia-related SNPs exhibited significant global structural changes, 19.53% presented noteworthy local structural disturbance and there were wide-ranging structural perturbations in the splice-related motifs. We established a comprehensive evaluation system for structural disturbance in the splicing-related motifs and gave the priority ranking for the SNPs at RNA structural level. These high-priority SNPs were revealed to widely disturb the molecular interaction properties between splicing-related proteins and pre-mRNAs by HDOCK. Moreover, mini-gene assays confirmed that structural perturbation could influence splicing efficiency through structural remodelling. This study deepens our understanding of the potential molecular regulatory mechanisms of susceptible SNPs in myopia and contributes to personalized diagnosis, personalized medicine, disease-risk prediction and functional verification study by guiding the prioritization of the susceptibility SNPs.

Kinesin light chain 1 stabilizes insulin receptor substrate 1 to regulate the IGF-1-AKT signaling pathway during myoblast differentiation.

The IGF signaling pathway plays critical role in regulating skeletal myogenesis. We have demonstrated that KIF5B, the heavy chain of kinesin-1 motor, promotes myoblast differentiation through regulating IGF-p38MAPK activation. However, the roles of the kinesin light chain (Klc) in IGF pathway and myoblast differentiation remain elusive. In this study, we found that Klc1 was upregulated during muscle regeneration and downregulated in senescence mouse muscles and dystrophic muscles from mdx (X-linked muscular dystrophic) mice. Gain- and loss-of-function experiments further displayed that Klc1 promotes AKT-mTOR activity and positively regulates myogenic differentiation. We further identified that the expression levels of IRS1, the critical node of IGF-1 signaling, are downregulated in Klc1-depleted myoblasts. Coimmunoprecipitation study revealed that IRS1 interacted with the 88-154 amino acid sequence of Klc1 via its PTB domain. Notably, the reduced Klc1 levels were found in senescence and osteoporosis skeletal muscle samples from both mice and human. Taken together, our findings suggested a crucial role of Klc1 in the regulation of IGF-AKT pathway during myogenesis through stabilizing IRS1, which might ultimately influence the development of muscle-related disorders.

The ubiquitin ligase HERC4 suppresses MafA transcriptional activity triggered by GSK3ß in myeloma by atypical K63-linked polyubiquitination.

MafA and c-Maf are close members of the Maf transcription factor family and indicators of poor prognosis of multiple myeloma (MM). Our previous study finds that the ubiquitin ligase HERC4 induces c-Maf degradation but stabilizes MafA, and the mechanism is elusive. In the present study, we find that HERC4 interacts with MafA and mediates its K63-linked polyubiquitination at K33. Moreover, HERC4 inhibits MafA phosphorylation and its transcriptional activity triggered by glycogen synthase kinase 3ß (GSK3ß). The K33R MafA variant prevents HERC4 from inhibiting MafA phosphorylation and increases MafA transcriptional activity. Further analyses reveal that MafA can also activate the STAT3 signaling, but it is suppressed by HERC4. Lastly, we demonstrate that lithium chloride, a GSK3ß inhibitor, can upregulate HERC4 and synergizes dexamethasone, a typical anti-MM drug, in inhibiting MM cell proliferation and xenograft growth in nude mice. These findings thus highlight a novel regulation of MafA oncogenic activity in MM and provide the rationale by targeting HERC4/GSK3ß/MafA for the treatment of MM.

Biomimetic-Structured Cobalt Nanocatalyst Suppresses Aortic Dissection Progression by Catalytic Antioxidation.

As one of the most lethal cardiovascular diseases, aortic dissection (AD) is initiated by overexpression of reactive oxygen species (ROS) in the aorta that damages the vascular structure and finally leads to massive hemorrhage and sudden death. Current drugs used in clinics for AD treatment fail to efficiently scavenge ROS to a large extent, presenting undesirable therapeutic effect. In this work, a nanocatalytic antioxidation concept has been proposed to elevate the therapeutic efficacy of AD by constructing a cobalt nanocatalyst with a biomimetic structure that can scavenge pathological ROS in an efficient and sustainable manner. Theoretical calculations demonstrate that the antioxidation reaction is catalyzed by the redox transition between hydroxocobalt(III) and oxo-hydroxocobalt(V) accompanied by inner-sphere proton-coupled two-electron transfer, forming a nonassociated activation catalytic cycle. The efficient antioxidation action of the biomimetic nanocatalyst in the AD region effectively alleviates oxidative stress, which further modulates the aortic inflammatory microenvironment by promoting phenotype transition of macrophages. Consequently, vascular smooth muscle cells are also protected from inflammation in the meantime, suppressing AD progression. This study provides a nanocatalytic antioxidation approach for the efficient treatment of AD and other cardiovascular diseases.

Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Photoresponsive Hydrogen-Bonded Organic Frameworks-Enabled Organic Photoelectrochemical Transistors for Sensitive Bioanalysis.

A facile route for exponential magnification of transconductance (gm) in an organic photoelectrochemical transistor (OPECT) is still lacking. Herein, photoresponsive hydrogen-bonded organic frameworks (PR-HOFs) have been shown to be efficient for gm magnification in a typical poly(ethylene dioxythiophene):poly(styrenesulfonate) OPECT. Specifically, 450 nm light stimulation of 1,3,6,8-tetrakis (p-benzoic acid) pyrene (H4TBAPy)-based HOF could efficiently modulate the device characteristics, leading to the considerable gm magnification over 78 times from 0.114 to 8.96 mS at zero Vg. In linkage with a DNA nanomachine-assisted steric hindrance amplification strategy, the system was then interfaced with the microRNA-triggered structural DNA evolution toward the sensitive detection of a model target microRNA down to 0.1 fM. This study first reveals HOFs-enabled efficient gm magnification in organic electronics and its application for sensitive biomolecular detection.

US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

Dynamical Janus-Like Behavior Excited by Passive Cold-Heat Modulation in the Earth-Sun/Universe System: Opportunities and Challenges.

The utilization of solar-thermal energy and universal cold energy has led to many innovative designs that achieve effective temperature regulation in different application scenarios. Numerous studies on passive solar heating and radiation cooling often operate independently (or actively control the conversion) and lack a cohesive framework for deep connections. This work provides a concise overview of the recent breakthroughs in solar heating and radiation cooling by employing a mechanism material in the application model. Furthermore, the utilization of dynamic Janus-like behavior serves as a novel nexus to elucidate the relationship between solar heating and radiation cooling, allowing for the analysis of dynamic conversion strategies across various applications. Additionally, special discussions are provided to address specific requirements in diverse applications, such as optimizing light transmission for clothing or window glass. Finally, the challenges and opportunities associated with the development of solar heating and radiation cooling applications are underscored, which hold immense potential for substantial carbon emission reduction and environmental preservation. This work aims to ignite interest and lay a solid foundation for researchers to conduct in-depth studies on effective and self-adaptive regulation of cooling and heating.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

The molecular characteristics could supplement the staging system of pT2/T3N0M0 esophageal squamous cell carcinoma: a translational study based on a cohort with over 20 years of follow-up.

OBJECTIVE: This study aimed to construct a model based on 23 enrolled molecules to evaluate prognoses of pT2/3N0M0 esophageal squamous cell carcinoma (ESCC) patients with up to 20 years of follow-up. METHODS: The lasso-Cox model was used to identify the candidate molecule. A nomogram was conducted to develop the survival model (molecular score, MS) based on the molecular features. Cox regression and Kaplan-Meier analysis were used in this study. The concordance index (C-index) was measured to compare the predicted ability between different models. The primary endpoint was overall survival (OS). RESULTS: A total of 226 patients and 23 proteins were enrolled in this study. Patients were classified into high-risk (MS-H) and low-risk (MS-L) groups based on the MS score of 227. The survival curves showed that the MS-L cohort had better 5-year and 10-year survival rates than the MS-H group (5-year OS: 51.0% vs. 8.0%; 10-year OS: 45.0% vs. 5.0%, all p < 0.001). Furthermore, multivariable analysis confirmed MS as an independent prognostic factor after eliminating the confounding factors (Hazard ratio 3.220, p < 0.001). The pT classification was confirmed to differentiate ESCC patients' prognosis (Log-rank: p = 0.029). However, the combination of pT and MS could classify survival curves evidently (overall p < 0.001), which showed that the prognostic prediction efficiency was improved significantly by the combination of the pT and MS than by the classical pT classification (C-index: 0.656 vs. 0.539, p < 0.001). CONCLUSIONS: Our study suggested an MS for significant clinical stratification of T2/3N0M0 ESCC patients to screen out subgroups with poor prognoses. Besides, the combination of pT staging and MS could predict survival more accurately for this cohort than the pT staging system alone.

RGD-p21Ras-scFv expressed prokaryotically on a pilot scale inhibits ras-driven colorectal cancer growth by blocking p21Ras-GTP.

BACKGROUND: Ras gene mutation and/or overexpression are drivers in the progression of cancers, including colorectal cancer. Blocking the Ras signaling has become a significant strategy for cancer therapy. Previously, we constructed a recombinant scFv, RGD-p21Ras-scFv by linking RGD membrane-penetrating peptide gene with the anti-p21Ras scFv gene. Here, we expressed prokaryotically RGD-p21Ras-scFv on a pilot scale, then investigated the anti-tumor effect and the mechanism of blocking Ras signaling. METHODS: The E. coli bacteria which could highly express RGD-p21Ras-scFv was screened and grown in 100 L fermentation tank to produce RGD-p21Ras-scFv on optimized induced expression conditions. The scFv was purified from E. coli bacteria using His Ni-NTA column. ELISA was adopted to test the immunoreactivity of RGD-p21Ras-scFv against p21Ras proteins, and the IC50 of RGD-p21Ras-scFv was analyzed by CCK-8. Immunofluorescence colocalization and pull-down assays were used to determine the localization and binding between RGD-p21Ras-scFv and p21Ras. The interaction forces between RGD-p21Ras-scFv and p21Ras after binding were analyzed by molecular docking, and the stability after binding was determined by molecular dynamics simulations. p21Ras-GTP interaction was detected by Ras pull-down. Changes in the MEK-ERK /PI3K-AKT signaling paths downstream of Ras were detected by WB assays. The anti-tumor activity of RGD-p21Ras-scFv was investigated by nude mouse xenograft models. RESULTS: The technique of RGD-p21Ras-scFv expression on a pilot scale was established. The wet weight of the harvested bacteria was 31.064 g/L, and 31.6 mg RGD-p21Ras-scFv was obtained from 1 L of bacterial medium. The purity of the recombinant antibody was above 85%, we found that the prepared on a pilot scale RGD-p21Ras-scFv could penetrate the cell membrane of colon cancer cells and bind to p21Ras, then led to reduce of p21Ras-GTP (active p21Ras). The phosphorylation of downstream effectors MEK-ERK /PI3K-AKT was downregulated. In vivo antitumor activity assays showed that the RGD-p21Ras-scFv inhibited the proliferation of colorectal cancer cell lines. CONCLUSION: RGD-p21Ras-scFv prokaryotic expressed on pilot-scale could inhibited Ras-driven colorectal cancer growth by partially blocking p21Ras-GTP and might be able to be a hidden therapeutic antibody for treating RAS-driven tumors.

Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.

BACKGROUND: The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS: In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS: The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS: We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Association of congenital iodine deficiency syndrome and differentiated thyroid cancer: a Mendelian randomization study.

BACKGROUND: The effect of iodine deficiency, especially during the fetal period, on thyroid cancer risk remains unclear. The evidence from observational studies is controversial because of the inevitable confounding factors. We studied the causal effect of congenital iodine deficiency on differentiated thyroid cancer (DTC) based on Mendelian randomization (MR). METHODS: Two-Sample MR analysis was performed using data from published genome-wide association studies, including congenital iodine deficiency syndrome (CIDS) (353 cases, 187,684 controls) and DTC (649 cases, 431 controls) data. RESULTS: There was a causal relationship between CIDS and DTC (P < 0.05), with CIDS increasing the DTC risk by 37.4% (OR = 1.374, 95%CI = 1.110-1.700). Heterogeneity tests and tests of multiple validities indicated that the results were solid and reliable (all P < 0.05). CONCLUSIONS: Fetal iodine deficiency increases the risk of DTC, so future clinical studies should focus on the effect of iodine supplementation during pregnancy to reduce the risk of thyroid cancer in the offspring.

Toll-interacting protein is activated by the transcription factor GATA1 and Sp1 to negatively regulate NF-κB and MAPK pathways in the Japanese eel (Anguilla japonica).

Toll-interacting protein (Tollip) serves as a crucial inhibitory factor in the modulation of Toll-like receptor (TLR)-mediated innate immunological responses. The structure and function of Tollip have been well documented in mammals, yet the information in teleost remained limited. This work employed in vitro overexpression and RNA interference in vivo and in vitro to comprehensively examine the regulatory effects of AjTollip on NF-κB and MAPK signaling pathways. The levels of p65, c-Fos, c-Jun, IL-1, IL-6, and TNF-α were dramatically reduced following overexpression of AjTollip, whereas knocking down AjTollip in vivo and in vitro enhanced those genes' expression. Protein molecular docking simulations showed AjTollip interacts with AjTLR2, AjIRAK4a, and AjIRAK4b. A better understanding of the transcriptional regulation of AjTollip is crucial to elucidating the role of Tollip in fish antibacterial response. Herein, we cloned and characterized a 2.2 kb AjTollip gene promoter sequence. The transcription factors GATA1 and Sp1 were determined to be associated with the activation of AjTollip expression by using promoter truncation and targeted mutagenesis techniques. Collectively, our results indicate that AjTollip suppresses the NF-κB and MAPK signaling pathways, leading to the decreased expression of the downstream inflammatory factors, and GATA1 and Sp1 play a vital role in regulating AjTollip expression.

Uranium Biogeochemistry in the Rhizosphere of a Contaminated Wetland.

The objective of this study was to determine if U sediment concentrations in a U-contaminated wetland located within the Savannah River Site, South Carolina, were greater in the rhizosphere than in the nonrhizosphere. U concentrations were as much as 1100% greater in the rhizosphere than in the nonrhizosphere fractions; however and importantly, not all paired samples followed this trend. Iron (but not C, N, or S) concentrations were significantly enriched in the rhizosphere. XAS analyses showed that in both sediment fractions, U existed as UO22+ coordinated with iron(III)-oxides and organic matter. A key difference between the two sediment fractions was that a larger proportion of U was adsorbed to Fe(III)-oxides, not organic matter, in the rhizosphere, where significantly greater total Fe concentrations and greater proportions of ferrihydrite and goethite existed. Based on 16S rRNA analyses, most bacterial sequences in both paired samples were heterotrophs, and population differences were consistent with the generally more oxidizing conditions in the rhizosphere. Finally, U was very strongly bound to the whole (unfractionated) sediments, with an average desorption Kd value (Usediment/Uaqueous) of 3972 ± 1370 (mg-U/kg)/(mg-U/L). Together, these results indicate that the rhizosphere can greatly enrich U especially in wetland areas, where roots promote the formation of reactive Fe(III)-oxides.

Exploring the impact of insufficient thermal ablation on hepatocellular carcinoma: NDST2 overexpression mechanism and its role in facilitating growth and invasion of residual cancer cells.

OBJECTIVE: Incomplete thermal ablation (ITA) fosters the malignancy of residual cells in Hepatocellular carcinoma (HCC) with unclear mechanisms now. This study aims to investigate the expression changes of NDST2 following ITA of HCC and its impact on residual cancer cells. METHODS: An in vitro model of heat stress-induced liver cancer was constructed to measure the expression of NDST2 using Quantitative Real-Time PCR and Western blotting experiments. The sequencing data from nude mice were used for validation. The clinical significance of NDST2 in HCC was evaluated by integrating datasets. Gene ontology and pathway analysis were conducted to explore the potential signaling pathways regulated by NDST2. Additionally, NDST2 was knocked down in heat stress-induced HCC cells, and the effects of NDST2 on these cells were verified using Cell Counting Kit-8 assays, scratch assays, and Transwell assays. RESULTS: NDST2 expression levels are elevated in HCC, leading to a decrease in overall survival rates of HCC patients. Upregulation of immune checkpoint levels in high NDST2-expressing HCC may contribute to immune evasion by liver cancer cells. Additionally, the low mutation rate of NDST2 in HCC suggests a relatively stable expression of NDST2 in this disease. Importantly, animal and cell models treated with ITA demonstrate upregulated expression of NDST2. Knockdown of NDST2 in heat stress-induced liver cancer cells results in growth inhibition associated with gene downregulation. CONCLUSION: The upregulation of NDST2 can accelerate the progression of residual HCC after ITA, suggesting a potential role for NDST2 in the therapeutic efficacy and prognosis of residual HCC.

Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model. MATERIALS AND METHODS: This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors' region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models' overall performance. RESULTS: The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481-0.868), CEUS 0.821 (95%CI: 0.660-0.983), Combined_1 0.829 (95%CI: 0.673-0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509-0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC. CONCLUSION: The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.

Risk factors and predicting nomogram for the clinical deterioration of non-severe community-acquired pneumonia.

BACKGROUND: Currently, there remains insufficient focus on non-severe community-acquired pneumonia (CAP) patients who are at risk of clinical deterioration, and there is also a dearth of research on the related risk factors. Early recognition of hospitalized patients at risk of clinical deterioration will be beneficial for their clinical management. METHOD: A retrospective study was conducted in The First Affiliated Hospital of Wenzhou Medical University, China, spanning from January 1, 2018 to April 30, 2022, and involving a total of 1,632 non-severe CAP patients. Based on whether their condition worsened within 72 h of admission, patients were divided into a clinical deterioration group and a non-clinical deterioration group. Additionally, all patients were randomly assigned to a training set containing 75% of patients and a validation set containing 25% of patients. In the training set, risk factors for clinical deterioration in patients with non-severe CAP were identified by using LASSO regression analysis and multivariate logistic regression analysis. A nomogram was developed based on identified risk factors. The effectiveness of the nomogram in both the training and validation sets was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: Age, body mass index (BMI), body temperature, cardiovascular comorbidity, respiratory rate, LDH level, lymphocyte count and D-dimer level were identified as risk factors associated with the clinical deterioration of non-severe CAP within 72 h of admission. The area under curve (AUC) value of the nomogram was 0.78 (95% CI: 0.74-0.82) in the training set and 0.75 (95% CI: 0.67-0.83) in the validation set. Furthermore, the calibration curves for both the training and validation sets indicated that the predicted probability of clinical deterioration aligned with the actual probability. Additionally, DCA revealed clinical utility for the nomogram at a specific threshold probability. CONCLUSION: The study successfully identified the risk factors linked to the clinical deterioration of non-severe CAP and constructed a nomogram for predicting the probability of deterioration. The nomogram demonstrated favorable predictive performance and has the potential to aid in the early identification and management of non-severe CAP patients at elevated risk of deterioration.

Endoscopic Rectal Ultrasound-Based Radiomics Analysis for the Prediction of Synchronous Liver Metastasis in Patients With Primary Rectal Cancer.

OBJECTIVES: To develop and validate an ultrasound-based radiomics model to predict synchronous liver metastases (SLM) in rectal cancer (RC) patients preoperatively. METHODS: Two hundred and thirty-nine RC patients were included in this study and randomly divided into training and validation cohorts. A total of 5936 radiomics features were calculated on the basis of ultrasound images to build a radiomic model and obtain a radiomics score (Rad-score) using logistic regression. Meanwhile, clinical characteristics were collected to construct a clinical model. The radiomics-clinical model was developed and validated by integrating the radiomics features with the selected clinical characteristics. The performances of three models were evaluated and compared through their discrimination, calibration, and clinical usefulness. RESULTS: The radiomics model was developed based on 13 radiomic features. The radiomics-clinical model, which incorporated Rad-score, CEA, and CA199, exhibited favorable discrimination and calibration with areas under the receiver operating characteristic curve (AUC) of 0.920 (95% CI: 0.874-0.965) in the training cohorts and 0.855 (95% CI: 0.759-0.951) in the validation cohorts. And the AUC of the radiomics-clinical model was 0.849 (95% CI: 0.771-0.927) for the training cohorts and 0.780 (95% CI: 0.655-0.905) for the validation cohorts, the clinical model was 0.811 (95% CI: 0.718-0.905) for the training cohorts and 0.805 (95% CI: 0.645-0.965) for the validation cohorts. Moreover, decision curve analysis (DCA) further confirmed the clinical utility of the radiomics-clinical model. CONCLUSIONS: The radiomics-clinical model performed satisfactory predictive performance, which can help improve clinical diagnosis performance and outcome prediction for SLM in RC patients.