A High-Avidity, Thermostable, and Low-Cost Synthetic Capture for Ultrasensitive Detection and Quantification of Viral Antigens and Aerosols.

Lateral flow assays (LFAs) are currently the most popular point-of-care diagnostics, rapidly transforming disease diagnosis from expensive doctor checkups and laboratory-based tests to potential on-the-shelf commodities. Yet, their sensitive element, a monoclonal antibody, is expensive to formulate, and their long-term storage depends on refrigeration technology that cannot be met in resource-limited areas. In this work, LCB1 affibodies (antibody mimetic miniproteins) were conjugated to bovine serum albumin (BSA) to afford a high-avidity synthetic capture (LCB1-BSA) capable of detecting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and virus like particles (VLPs). Substituting the monoclonal antibody 2B04 for LCB1-BSA (stable up to 60 °C) significantly improved the thermal stability, shelf life, and affordability of plasmonic-fluor-based LFAs (p-LFAs). Furthermore, this substitution significantly improved the sensitivity of p-LFAs toward the spike protein and VLPs with precise quantitative ability over 2 and 3 orders of magnitude, respectively. LCB1-BSA sensors could detect VLPs at 100-fold lower concentrations, and this improvement, combined with their robust nature, enabled us to develop an aerosol sampling technology to detect aerosolized viral particles. Synthetic captures like LCB1-BSA can increase the ultrasensitivity, availability, sustainability, and long-term accuracy of LFAs while also decreasing their manufacturing costs.

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification.

A crucial challenge in critical settings like medical diagnosis is making deep learning models used in decision-making systems interpretable. Efforts in Explainable Artificial Intelligence (XAI) are underway to address this challenge. Yet, many XAI methods are evaluated on broad classifiers and fail to address complex, real-world issues, such as medical diagnosis. In our study, we focus on enhancing user trust and confidence in automated AI decision-making systems, particularly for diagnosing skin lesions, by tailoring an XAI method to explain an AI model's ability to identify various skin lesion types. We generate explanations using synthetic images of skin lesions as examples and counterexamples, offering a method for practitioners to pinpoint the critical features influencing the classification outcome. A validation survey involving domain experts, novices, and laypersons has demonstrated that explanations increase trust and confidence in the automated decision system. Furthermore, our exploration of the model's latent space reveals clear separations among the most common skin lesion classes, a distinction that likely arises from the unique characteristics of each class and could assist in correcting frequent misdiagnoses by human professionals.

Exosome secretion related gene signature predicts chemoresistance in patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a highly heterogeneous malignancy, and patients often have different responses to treatment. In this study, the genetic characteristics related to exosome formation and secretion procedure were used to predict chemoresistance and guide the individualized treatment of patients. METHODS: Firstly, seven microarray datasets in Gene Expression Omnibus (GEO) and RNA-Seq dataset from the Cancer Genome Atlas (TCGA) were used to analysis the transcriptome profiles and associated characteristics of CRC patients. Then, a predictive model based on gene features linked to exosome formation and secretion was created and validated using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning. Finally, we evaluated the model using chemoresistant/chemosensitive cells and tissues by immunofluorescence (IF), western blot (WB), quantitative real-time PCR (qRT-PCR) and immunocytochemistry (IHC) experiments, and the predictive value of integrated model in the clinical validation cohort were performed by Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) curves analyses. RESULTS: We established a risk score signature based on three genes related to exosome secretion in CRC. Better Overall Survival (OS) and greater chemosensitivity were seen in the low-risk group, whereas the high-risk group exhibited chemoresistance and a subpar response to immune checkpoint blockade (ICB) therapy. Higher expression of the model genes EXOC2, EXOC3 and STX4 were observed in chemoresistant cells and specimens. The AUC of 5-year disease-free survival (DFS) was 0.804. Compared with that in the low-risk group, patients' DFS was found to be significantly worse in the high-risk group. CONCLUSIONS: In summary, the gene signature related to exosome formation and secretion could reliably predict patients' chemosensitivity and ICB treatment response, which providing new independent biomarkers for the treatment of CRC.

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.

BACKGROUND: Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. AIM: To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. METHODS: A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. RESULTS: The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. CONCLUSION: Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Clinical significance of small extracellular vesicles in cholangiocarcinoma.

Cholangiocarcinoma is an aggressive and heterogeneous malignancy originating from the bile duct epithelium. It is associated with poor prognosis and high mortality. The global incidence of cholangiocarcinoma is rising, and there is an urgent need for effective early diagnosis and treatment strategies to reduce the burden of this devastating tumor. Small extracellular vesicles, including exosomes and microparticles, are nanoscale vesicles formed by membranes that are released both normally and pathologically from cells, mediating the intercellular transfer of substances and information. Recent studies have demonstrated the involvement of small extracellular vesicles in numerous biological processes, as well as the proliferation, invasion, and metastasis of tumor cells. The present review summarizes the tumorigenic roles of small extracellular vesicles in the cholangiocarcinoma microenvironment. Owing to their unique composition, accessibility, and stability in biological fluids, small extracellular vesicles have emerged as ideal biomarkers for use in liquid biopsies for diagnosing and outcome prediction of cholangiocarcinoma. Specific tissue tropism, theoretical biocompatibility, low clearance, and strong biological barrier penetration of small extracellular vesicles make them suitable drug carriers for cancer therapy. Furthermore, the potential value of small extracellular vesicle-based therapies for cholangiocarcinoma is also reviewed.

The heterogeneity of tumour-associated macrophages contributes to the clinical outcomes and indications for immune checkpoint blockade in colorectal cancer patients.

Tumor-associated macrophages (TAMs), one of the major immune cell types in colorectal cancer (CRC) tumor microenvironment (TME), play indispensable roles in immune responses against tumor progression. In this study, we aimed to know whether the extensive inter and intra heterogeneity of TAMs contributes to the clinical outcomes and indications for immune checkpoint blockade (ICB) in CRC. We used single-cell RNA sequencing (scRNA-Seq) data from 60 CRC patients and charactrized TAMs based on anatomic locations, tumor regions, stages, grades, metastatic status, MSS/MSI classification and pseudotemporal differentiation status. We then defined a catalog of 21 gene modules that determine macrophage status, and identified 7 of them as relevant to clinical outcomes and 11 as indications for ICB therapy. On this basis, we constructed a unique TAM subgroup profile, aiming to find features that may be highly responsive to immunotherapy for the CRC with poor prognosis under conventional treatment. This TAM subpopulation is enriched in tumors and is associated with poor prognosis, but exhibits a high immunotherapy response signature (HIM TAM). Further spatial transcriptome analysis and ligand-receptor interaction analysis confirmed that HIM TAM is involved in shaping TIME, especially the regulation of T cells. Our study provides insights into different TAM subtypes, highlights the importance of TAM heterogeneity in relation to patient prognosis and immunotherapy response, and reveals potential immunotherapy strategies based on TAM characteristics for CRC that does not respond well to conventional therapy.

Neoadjuvant and/or adjuvant chemotherapy for gastric cancer patients with microsatellite instability or deficient mismatch repair: a systematic review and meta-analysis study protocol.

INTRODUCTION: Whether gastric cancer (GC) patients with deficient mismatch repair or microsatellite instability-high (dMMR/MSI-H) benefit from perioperative (neoadjuvant and/or adjuvant) chemotherapy is controversial. This protocol delineates the planned scope and methods for a systematic review and meta-analysis that aims to compare the efficacy of perioperative chemotherapy with surgery alone in resectable dMMR/MSI-H GC patients. METHODS AND ANALYSIS: This study protocol is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols-P guideline. PubMed, Embase, Cochrane (CENTRAL), and the Web of Science databases will be searched, supplemented by a secondary screening of relevant records. Both randomised controlled trials and non-randomised studies will be included in this study. The primary and secondary outcomes under scrutiny will be overall survival, disease-free survival and progression-free survival. Two reviewers will independently screen studies, extract data and assess the risk of bias. We will analyse different treatment settings (eg, neoadjuvant or adjuvant or combined as perioperative chemotherapies) separately and conduct sensitivity analyses. ETHICS AND DISSEMINATION: No ethics approval is required for this systematic review and meta-analysis, as no individual patient data will be collected. The findings of our study will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42023494276.

Activation of the FOXM1/ASF1B/PRDX3 axis confers hyperproliferative and antioxidative stress reactivity to gastric cancer.

Nucleosome assembly during DNA replication is dependent on histone chaperones. Recent studies suggest that dysregulated histone chaperones contribute to cancer progression, including gastric cancer (GC). Further studies are required to explore the prognostic and therapeutic implications of histone chaperones and their mechanisms of action in GC progression. Here we identified histone chaperone ASF1B as a potential biomarker for GC proliferation and prognosis. ASF1B was significantly upregulated in GC, which was associated with poor prognosis. In vitro and in vivo experiments demonstrated that the inhibition of ASF1B suppressed the malignant characteristics of GC, while overexpression of ASF1B had the opposite effect. Mechanistically, transcription factor FOXM1 directly bound to the ASF1B-promoter region, thereby regulating its transcription. Treatment with thiostrepton, a FOXM1 inhibitor, not only suppressed ASF1B expression, but also inhibited GC progression. Furthermore, ASF1B regulated the mitochondrial protein peroxiredoxin 3 (PRDX3) transcription in a FOXM1-dependent manner. The crucial role of ASF1B-regulated PRDX3 in GC cell proliferation and oxidative stress balance was also elucidated. In summary, our study suggests that the FOXM1-ASF1B-PRDX3 axis is a potential therapeutic target for treating GC.

Harnessing Mechanical Stress with Viscoelastic Biomaterials for Periodontal Ligament Regeneration.

The viscoelasticity of mechanically sensitive tissues such as periodontal ligaments (PDLs) is key in maintaining mechanical homeostasis. Unfortunately, PDLs easily lose viscoelasticity (e.g., stress relaxation) during periodontitis or dental trauma, which disrupt cell-extracellular matrix (ECM) interactions and accelerates tissue damage. Here, Pluronic F127 diacrylate (F127DA) hydrogels with PDL-matched stress relaxation rates and high elastic moduli are developed. The hydrogel viscoelasticity is modulated without chemical cross-linking by controlling precursor concentrations. Under cytomechanical loading, F127DA hydrogels with fast relaxation rates significantly improved the fibrogenic differentiation potential of PDL stem cells (PDLSCs), while cells cultured on F127DA hydrogels with various stress relaxation rates exhibited similar fibrogenic differentiation potentials with limited cell spreading and traction forces under static conditions. Mechanically, faster-relaxing F127DA hydrogels leveraged cytomechanical loading to activate PDLSC mechanotransduction by upregulating integrin-focal adhesion kinase pathway and thus cytoskeletal rearrangement, reinforcing cell-ECM interactions. In vivo experiments confirm that faster-relaxing F127DA hydrogels significantly promoted PDL repair and reduced abnormal healing (e.g., root resorption and ankyloses) in delayed replantation of avulsed teeth. This study firstly investigated how matrix nonlinear viscoelasticity influences the fibrogenesis of PDLSCs under mechanical stimuli, and it reveals the underlying mechanobiology, which suggests novel strategies for PDL regeneration.

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist.

The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.

Transition in metabolic health phenotypes across general adiposity categories and association with the risk of depression: a prospective analysis.

BACKGROUND: The association between obesity and depression may partly depend on the contextual metabolic health. The effect of change in metabolic health status over time on subsequent depression risk remains unclear. We aimed to assess the prospective association between metabolic health and its change over time and the risk of depression across body mass index (BMI) categories. METHODS: Based on a nationally representative cohort, we included participants enrolled at the wave 2 (2004-2005) of the English Longitudinal Study of Ageing and with follow-up for depression at wave 8 (2016-2017). Participants were cross-classified by BMI categories and metabolic health (defined by the absence of hypertension, diabetes, and hypercholesterolemia) at baseline or its change over time (during waves 3-6). Logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of depression at follow-up stratified by BMI category and metabolic health status with adjustment for potential confounders. RESULTS: The risk of depression was increased for participants with metabolically healthy obesity compared with healthy nonobese participants, and the risk was highest for those with metabolically unhealthy obesity (OR 1.62, 95% CI 1.18-2.20). Particularly hypertension and diabetes contribute most to the increased risk. The majority of metabolically healthy participants converted to unhealthy metabolic phenotype (50.1% of those with obesity over 8 years), which was associated with an increased risk of depression. Participants who maintained metabolically healthy obesity were still at higher risk (1.99, 1.33-2.72), with the highest risk observed for those with stable unhealthy metabolic phenotypes. CONCLUSIONS: Obesity remains a risk factor for depression, independent of whether other metabolic risk factors are present or whether participants convert to unhealthy metabolic phenotypes over time. Long-term maintenance of metabolic health and healthy body weight may be beneficial for the population mental well-being.

Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network.

Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identification and risk stratification of patients with HF. The first study, with a case-control study design to address data imbalance issue, included 431 subjects with HF exhibiting typical symptoms and 1545 control participants with no history of HF (non-HF). Carotid pressure waveforms were obtained from all the participants using applanation tonometry. The HF score, representing the probability of HF, was derived from a one-dimensional deep neural network (DNN) model trained with characteristics of the normalized carotid pressure waveform. In the second study of HF patients, we constructed a Cox regression model with 83 candidate clinical variables along with the HF score to predict the risk of all-cause mortality along with rehospitalization. To identify subjects using the HF score, the sensitivity, specificity, accuracy, F1 score, and area under receiver operating characteristic curve were 0.867, 0.851, 0.874, 0.878, and 0.93, respectively, from the hold-out cross-validation of the DNN, which was better than other machine learning models, including logistic regression, support vector machine, and random forest. With a median follow-up of 5.8 years, the multivariable Cox model using the HF score and other clinical variables outperformed the other HF risk prediction models with concordance index of 0.71, in which only the HF score and five clinical variables were independent significant predictors (p < 0.05), including age, history of percutaneous coronary intervention, concentration of sodium in the emergency room, N-terminal pro-brain natriuretic peptide, and hemoglobin. Our study demonstrated the diagnostic and prognostic utility of arterial waveforms in subjects with HF using a DNN model. Pulse wave contains valuable information that can benefit the clinical care of patients with HF.

Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling.

BACKGROUND: There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days. METHODS: We recruited patients with respiratory failure at the First People's Hospital of Yancheng and the People's Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness. RESULTS: The LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value. CONCLUSIONS: A nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.

Extraction, structural characterization and immunoactivity of glucomannan type polysaccahrides from Lilium brownii var. viridulum Baker.

Homogeneous polysaccharide (LBP) was extracted and purified from the bulblets of Lilium brownii var. viridulum Baker with a molecular weight of 312 kDa. The monosaccharides are composed of mannose and glucose, and the corresponding molar ratios are 0.582 and 0.418, respectively. FT-IR, LC-MS, NMR, GC-MS and HPAEC were used to analyze the functional groups, glycosidic linkages and chemical structure of LBP, which was a 1-4-linked glucomannan and contained a dodecasaccharide repeating units of →4)-ß-D-Manp-(1 â†’ 4)-ß-D-Manp-(1 â†’ 4)-ß-D-Manp-(1 â†’ 4)-ß-D-Glcp-(1 â†’ 4)-ß-D-Manp-(1 â†’ 4)-ß-D-Manp-(1 â†’ 4)-ß-D-Glcp-(1 â†’ 4)-α-D-Glcp-(1 â†’ 4)-ß-D-Glcp-(1 â†’ 4)-ß-D-Glcp-(1 â†’ 4)-ß-D-Manp-(1 â†’ 4)-ß-D-Manp-(1 â†’ . In vitro experimental results showed that LBP had noble biocompatibility, and a low dose of 5 µg/mL LBP significantly up-regulated the mRNA expression of TNF-α, iNOS, IL-6, IL-1ß and Toll-like receptors family (TLRs) in RAW 264.7 cells. In conclusion, LBP played an important role in immunomodulation, and further studies on the specific immunomodulatory mechanisms of LBP on RAW 264.7 cells are still needed.

Reprogramming tumor-associated macrophages by a dually targeted milk exosome system as a potent monotherapy for cancer.

Tumor-associated macrophages (TAMs) play a key role in inducing an immunosuppressive tumor microenvironment (TME) and cancer immune escape. We previously revealed that PDL1 (a key immune checkpoint) was upregulated in TAMs and induced M2 polarization, highlighting PDL1 in TAMs as a promising cancer therapeutic target. In this study, we developed an engineered milk exosome (mExo) system decorated with M2pep (an M2 macrophage binding peptide) and 7D12 (an anti-EGFR nanobody) (7D12-mExo-M2pep-siPDL1) to specifically deliver siPDL1 into M2 TAMs. A series of in vitro and in vivo assays showed that the dually targeted engineered mExos efficiently delivered siPDL1 into M2 TAMs and repolarized them into M1 macrophages, restoring CD8+ T cell immune activity and remodeling TME. Importantly, systemically administered 7D12-mExo-M2pep-siPDL1 showed efficient single-agent antitumor activity, resulting in nearly 90% tumor growth inhibition in a mouse model of orthotopic epidermal growth factor receptor (EGFR) cancer. Collectively, our study indicates that PDL1 is a promising target for TAM-based cancer immunotherapy, and our engineered mExo-based nanomedicine represents a novel tool for specifically targeting M2 TAMs, distinguishing this novel therapeutic method from other TAM-targeting therapies and highlighting its promising clinical potential.

Deficiency of diacylglycerol Kinase ζ promotes Beclin1-mediated autophagy via the mTOR/TFEB signaling pathway: Relevance to maladaptive cardiac hypertrophy.

The activation Gq protein-coupled receptors (GPCRs) is a crucial factor contributing to maladaptive cardiac hypertrophy, and dysregulation of autophagy is implicated in its prohypertrophic effects. Previous studies have shown that diacylglycerol kinase zeta (DGKζ) can suppress cardiac hypertrophy by inhibiting the diacylglycerol (DAG)-PKC pathway in response to mechanical strain or growth agonists such as endothelin-1 (ET-1). However, the involvement of DGKζ in autophagy regulation remains poorly understood. In this study, we aimed to investigate the role of DGKζ in autophagy regulation during maladaptive cardiac hypertrophy. We found that Beclin1-mediated autophagy was involved in the development of maladaptive cardiac hypertrophy and dysfunction in response to prohypertrophic challenges of transverse aortic constriction (TAC) or ET-1. Deficiency of DGKζ promoted Beclin1-mediated autophagy, aggravated adverse cardiac remodeling, and cardiac dysfunction, which could be ameliorated by genetic deletion of Beclin1 or TFEB. Mechanistically, the deficiency of DGKζ disrupted the activation of AKT/mTOR signaling, the association between mTOR and TFEB, and favored the nuclear translocation of TFEB from the cytoplasm, leading to enhanced activation of Beclin1-mediated autophagy through ULK1/Beclin1 signaling and TFEB-dependent Beclin1 transcription. Taken together, these results suggest that the mechanisms by which DGKζ alleviates pathological cardiac hypertrophy may involve the regulation of Beclin1-mediated autophagy through the mTOR/TFEB signaling pathway.