Recurrent SARS-CoV-2 spike mutations confer growth advantages to select JN.1 sublineages.

The recently dominant SARS-CoV-2 Omicron JN.1 has evolved into multiple sublineages, with recurrent spike mutations R346T, F456L, and T572I, some of which exhibit growth advantages, such as KP.2 and KP.3. We investigated these mutations in JN.1, examining their individual and combined effects on immune evasion, ACE2 receptor affinity, and in vitro infectivity. F456L increased resistance to neutralization by human sera, including those after JN.1 breakthrough infections, and by RBD class-1 monoclonal antibodies, significantly altering JN.1 antigenicity. R346T enhanced ACE2-binding affinity and modestly boosted the infectivity of JN.1 pseudovirus, without a discernible effect on serum neutralization, while T572I slightly bolstered evasion of SD1-directed mAbs against JN.1's ancestor, BA.2, possibly by altering SD1 conformation. Importantly, expanding sublineages such as KP.2 containing R346T, F456L, and V1104L, showed similar neutralization resistance as JN.1 with R346T and F456L, suggesting V1104L does not appreciably affect antibody evasion. Furthermore, the hallmark mutation Q493E in KP.3 significantly reduced ACE2-binding affinity and viral infectivity, without noticeably impacting serum neutralization. Our findings illustrate how certain JN.1 mutations confer growth advantages in the population and could inform the design of the next COVID-19 vaccine booster.

In Situ Heparan Sulfate-Induced Peptide Self-Assembly to Overcome the Cell Surface Glycocalyx Barrier for Cancer Treatment.

Heparan sulfate (HS) is a major component of cell surface glycocalyx with extensive negative charges and plays a protective role by preventing toxins, including small molecule drugs and anticancer cationic lytic peptides (ACLPs), from cells. However, this effect may compromise the treatment efficiency of anticancer drugs. To overcome the impedance of cancer cell glycocalyx, an HS-targeting ACLP PTP-7z was designed by fusion of an ACLP and a Zn2+-binding HS-targeting peptide. Upon Zn2+ ion binding, PTP-7z could self-assemble into uniform nanoparticles and show improved serum stability and reduced hemolysis, which enable it to self-deliver to tumor sites. The peptide PTP-7z showed a pH- and Zn2+ ion-dependent HS-binding ability, which triggers the HS-induced in situ self-assembling on the cancer cell surface in the acidic tumor microenvironment (TME). The self-assembled PTP-7z can overcome the impedance of cell glycocalyx by either disrupting cell membranes or translocating into cells through endocytosis and inducing cell apoptosis. Moreover, PTP-7z can also inhibit cancer cell migration. These results proved that HS-responsive in situ self-assembling is a practical strategy to overcome the cancer cell glycocalyx barrier for ACLPs and could be extended to the design of other peptide drugs to promote their in vivo application.

Structural Study of Aqueous Electrolyte Solution by MeV Liquid Electron Scattering.

The impact of ions on water has long been a subject of great interest, as it is closely tied to the hydration structure, dynamics, and properties of electrolyte solutions. Over centuries of investigation, the influence of ions on water's structure remains highly debated. Prevailing techniques, such as neutron and X-ray scattering, primarily focus on the microscopic structure of salt solutions at very high concentrations, mostly above 1 mol/L. In this study, we measured the structure of aqueous potassium iodide (KI) and potassium chloride (KCl) solutions using MeV liquid electron scattering (MeV-LES) across a concentration range of 0.10 to 0.75 mol/L. The obtained results provide detailed insights into the variations in ion-oxygen and oxygen-oxygen correlations as a function of concentration. The observed structural differences between KI and KCl solutions are in line with the structure maker/breaker theory, which suggests that iodide ions exert a more pronounced effect than chloride ions on disrupting the water shell. This work demonstrates the potency of MeV-LES for investigating the atomic structure in liquids, augmenting the modern analytical toolbox.

Loncastuximab tesirine in Chinese patients with relapsed or refractory diffuse large B-cell lymphoma: a multicenter, open-label, single-arm, phase II trial.

Patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) have a poor prognosis. Loncastuximab tesirine (Lonca), an antibody conjugate targeting CD19, has demonstrated significant clinical benefit in R/R DLBCL in a global phase 2 LOTIS-2 study. In the China bridging pivotal phase 2 OL-ADCT-402-001 study, eligible patients aged ≥18 years with R/R DLBCL who had failed ≥ 2 lines of systemic therapies were enrolled and treated with Lonca every 3 week with 150 µg/kg for 2 cycles; then 75 µg/kg for subsequent cycles (up to 1 year). The primary endpoint was overall response rate (ORR) assessed by independent review committee. Primary analyses for efficacy and safety were performed on the patients who received at least one treatment and had at least 6 months of follow-up following an initial documented response. As of data-cutoff, 64 patients received Lonca (median: 4.0 cycles [range: 1 to 17]). The median number of prior lines of therapies was 3.0 (range: 2 to 12). The ORR was 51.6% (95% CI: 38.7% to 64.2%), and the complete response rate was 23.4%. Hematological events accounted for the majority of the most common (≥15%) Grade ≥3 treatment-emergent adverse events (TEAEs), in which increased gamma glutamyltransferase (25.0%), and hypokalaemia (18.8%) also were reported. Serious TEAEs were reported in 35 of 64 patients with 4 fatal TEAEs. In conclusion, Lonca monotherapy demonstrated clinically meaningful efficacy and was well-tolerated in heavily pretreated Chinese patients with R/R DLBCL, which was consistent with the results of the LOTIS-2 study in Caucasian patients.

Phenylalanine deprivation inhibits multiple myeloma progression by perturbing endoplasmic reticulum homeostasis.

Amino acid metabolic remodeling is a hallmark of cancer, driving an increased nutritional demand for amino acids. Amino acids are pivotal for energetic regulation, biosynthetic support, and homeostatic maintenance to stimulate cancer progression. However, the role of phenylalanine in multiple myeloma (MM) remains unknown. Here, we demonstrate that phenylalanine levels in MM patients are decreased in plasma but elevated in bone marrow (BM) cells. After the treatment, phenylalanine levels increase in plasma and decrease in BM. This suggests that changes in phenylalanine have diagnostic value and that phenylalanine in the BM microenvironment is an essential source of nutrients for MM progression. The requirement for phenylalanine by MM cells exhibits a similar pattern. Inhibiting phenylalanine utilization suppresses MM cell growth and provides a synergistic effect with Bortezomib (BTZ) treatment in vitro and murine models. Mechanistically, phenylalanine deprivation induces excessive endoplasmic reticulum stress and leads to MM cell apoptosis through the ATF3-CHOP-DR5 pathway. Interference with ATF3 significantly affects phenylalanine deprivation therapy. In conclusion, we have identified phenylalanine metabolism as a characteristic feature of MM metabolic remodeling. Phenylalanine is necessary for MM proliferation, and its aberrant demand highlights the importance of low-phenylalanine diets as an adjuvant treatment for MM.

Unraveling the spatial organization and development of human thymocytes through integration of spatial transcriptomics and single-cell multi-omics profiling.

The structural components of the thymus are essential for guiding T cell development, but a thorough spatial view is still absent. Here we develop the TSO-his tool, designed to integrate multimodal data from single-cell and spatial transcriptomics to decipher the intricate structure of human thymus. Specifically, we characterize dynamic changes in cell types and critical markers, identifying ELOVL4 as a mediator of CD4+ T cell positive selection in the cortex. Utilizing the mapping function of TSO-his, we reconstruct thymic spatial architecture at single-cell resolution and recapitulates classical cell types and their essential co-localization for T cell development; additionally, previously unknown co-localization relationships such as that of CD8αα with memory B cells and monocytes are identified. Incorporating VDJ sequencing data, we also delineate distinct intermediate thymocyte states during αß T cell development. Overall, these insights enhance our understanding of thymic biology and may inform therapeutic interventions targeting T cell-mediated immune responses.

Robust SARS-CoV-2-neutralizing antibodies sustained through 6 months post XBB.1.5 mRNA vaccine booster.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-neutralizing antibodies are substantially expanded 1 month after a shot of XBB.1.5 monovalent mRNA vaccine (XBB.1.5 MV) booster, but the durability of this response remains unknown. Here, we address this question by performing neutralization assays on four viral variants (D614G, BA.5, XBB.1.5, and JN.1) using sera from participants obtained at ∼1 month, ∼3 months, and ∼6 months post an XBB.1.5 MV booster. Our findings indicate that the resulting neutralizing antibody titers are robust and generally remain at stable levels for the study period, similar to those following XBB infection. Importantly, this durability of neutralizing antibody titers contrasts with the decline observed after a booster of the original monovalent or BA.5 bivalent mRNA vaccine. Our results are in line with the recent national data from the Centers for Disease Control and Prevention, showing that the efficacy against symptomatic SARS-CoV-2 infection is sustained for up to 4 months after an XBB.1.5 MV booster.

An interlaboratory proficiency test using metagenomic sequencing as a diagnostic tool for the detection of RNA viruses in swine fecal material.

Metagenomic shotgun sequencing (mNGS) can serve as a generic molecular diagnostic tool. An mNGS proficiency test (PT) was performed in six European veterinary and public health laboratories to detect porcine astroviruses in fecal material and the extracted RNA. While different mNGS workflows for the generation of mNGS data were used in the different laboratories, the bioinformatic analysis was standardized using a metagenomic read classifier as well as read mapping to selected astroviral reference genomes to assess the semiquantitative representation of astrovirus species mixtures. All participants successfully identified and classified most of the viral reads to the two dominant species. The normalized read counts obtained by aligning reads to astrovirus reference genomes by Bowtie2 were in line with Kraken read classification counts. Moreover, participants performed well in terms of repeatability when the fecal sample was tested in duplicate. However, the normalized read counts per detected astrovirus species differed substantially between participants, which was related to the different laboratory methods used for data generation. Further modeling of the mNGS data indicated the importance of selecting appropriate reference data for mNGS read classification. As virus- or sample-specific biases may apply, caution is needed when extrapolating this swine feces-based PT for the detection of other RNA viruses or using different sample types. The suitability of experimental design to a given pathogen/sample matrix combination, quality assurance, interpretation, and follow-up investigation remain critical factors for the diagnostic interpretation of mNGS results. IMPORTANCE: Metagenomic shotgun sequencing (mNGS) is a generic molecular diagnostic method, involving laboratory preparation of samples, sequencing, bioinformatic analysis of millions of short sequences, and interpretation of the results. In this paper, we investigated the performance of mNGS on the detection of porcine astroviruses, a model for RNA viruses in a pig fecal material, among six European veterinary and public health laboratories. We showed that different methods for data generation affect mNGS performance among participants and that the selection of reference genomes is crucial for read classification. Follow-up investigation remains a critical factor for the diagnostic interpretation of mNGS results. The paper contributes to potential improvements of mNGS as a diagnostic tool in clinical settings.

Efficacy and safety of standard BEACOPP regimen versus ABVD regimen for treatment of advanced Hodgkin's lymphoma.

INTRODUCTION: The current treatment regimens for Hodgkin's lymphoma (HL) are associated with high incidences of adverse events. PURPOSE: This study aimed to compare the efficacy and safety of doxorubicin + bleomycin + vincristine + dacarbazine (ABVD) and standard bleomycin + etoposide + doxorubicin + cyclophosphamide + vincristine + procarbazine + prednisone (BEACOPP) chemotherapy in the treatment of advanced stage HL. METHODS: This multicenter, randomized, parallel, open, positive control noninferiority trial was conducted from 2016 to 2019 and comprised 93 subjects who were randomized in a 1:1 ratio between the treatment (BEACOPP; n = 44) and control (ABVD; n = 49) groups. RESULTS: The primary efficacy endpoint of this trial was the objective response rate (ORR) after eight cycles of chemotherapy, which was 100.00% (36/36) in the treatment group and 95.74% (45/49) in the control group. The incidence of adverse reactions was 100% in both groups. Significant differences (P < 0.05) in the incidences of grade 3 (39/44 [88.64%] vs. 23/49 [46.94%]) and grade 4 (27/44 [61.36%] vs. 8/49 [16.94%]) adverse events were observed between the treatment and control groups, respectively. However, most of these reactions were manageable, with no serious consequences, and were reversible after discontinuation of the treatment. CONCLUSION: Both regimens had a similar ORR and were associated with a high number of adverse events. The ABVD regimen was better tolerated and safer than the standard BEACOPP regimen. This study indicates that the standard BEACOPP regimen may be considered as a treatment option for patients with advanced HL.

Machine learning-based prediction model for the efficacy and safety of statins.

Objective: The appropriate use of statins plays a vital role in reducing the risk of atherosclerotic cardiovascular disease (ASCVD). However, due to changes in diet and lifestyle, there has been a significant increase in the number of individuals with high cholesterol levels. Therefore, it is crucial to ensure the rational use of statins. Adverse reactions associated with statins, including liver enzyme abnormalities and statin-associated muscle symptoms (SAMS), have impacted their widespread utilization. In this study, we aimed to develop a predictive model for statin efficacy and safety based on real-world clinical data using machine learning techniques. Methods: We employed various data preprocessing techniques, such as improved random forest imputation and Borderline SMOTE oversampling, to handle the dataset. Boruta method was utilized for feature selection, and the dataset was divided into training and testing sets in a 7:3 ratio. Five algorithms, including logistic regression, naive Bayes, decision tree, random forest, and gradient boosting decision tree, were used to construct the predictive models. Ten-fold cross-validation and bootstrapping sampling were performed for internal and external validation. Additionally, SHAP (SHapley Additive exPlanations) was employed for feature interpretability. Ultimately, an accessible web-based platform for predicting statin efficacy and safety was established based on the optimal predictive model. Results: The random forest algorithm exhibited the best performance among the five algorithms. The predictive models for LDL-C target attainment (AUC = 0.883, Accuracy = 0.868, Precision = 0.858, Recall = 0.863, F1 = 0.860, AUPRC = 0.906, MCC = 0.761), liver enzyme abnormalities (AUC = 0.964, Accuracy = 0.964, Precision = 0.967, Recall = 0.963, F1 = 0.965, AUPRC = 0.978, MCC = 0.938), and muscle pain/Creatine kinase (CK) abnormalities (AUC = 0.981, Accuracy = 0.980, Precision = 0.987, Recall = 0.975, F1 = 0.981, AUPRC = 0.987, MCC = 0.965) demonstrated favorable performance. The most important features of LDL-C target attainment prediction model was cerebral infarction, TG, PLT and HDL. The most important features of liver enzyme abnormalities model was CRP, CK and number of oral medications. Similarly, AST, ALT, PLT and number of oral medications were found to be important features for muscle pain/CK abnormalities. Based on the best-performing predictive model, a user-friendly web application was designed and implemented. Conclusion: This study presented a machine learning-based predictive model for statin efficacy and safety. The platform developed can assist in guiding statin therapy decisions and optimizing treatment strategies. Further research and application of the model are warranted to improve the utilization of statin therapy.

Association between medication complexity and follow-up care attendance: insights from a retrospective multicenter cohort study across 1,223 Chinese hospitals.

Background: Patients with Chronic Obstructive Pulmonary Disease (COPD) frequently face substantial medication burdens. Follow-up care on medication management is critical in achieving disease control. This study aimed to analyze the complexity of COPD-specific medication and determine how it impacted patients' attendance on follow-up care. Methods: This multicenter study includes patients with COPD from 1,223 hospitals across 29 provinces in China from January 2021 to November 2022. The medication Regimen Complexity Index (MRCI) score was used to measure COPD-specific medication complexity. The association between medication complexity and follow-up care attendance was evaluated using the Cox Proportional Hazard Model. Results: Among 16,684 patients, only 2,306 (13.8%) returned for follow-up medication management. 20.3% of the patients had high complex medication regimen (MRCI score >15.0). The analysis revealed that compared to those with less complex regimens, patients with more complex medication regimens were significantly less likely to attend the follow-up medication care, with a Hazard Ratio (HR) of 0.82 (95% Confidence Interval [CI], 0.74-0.91). Specifically, patients with more complex dosage forms were 51% less likely to attend the follow-up care (95% CI, 0.43-0.57). This pattern was especially marked among male patients, patients younger than 65 years, and those without comorbid conditions. Conclusion: Higher medication complexity was associated with a decreased likelihood of attending follow-up care. To promote care continuity in chronic disease management, individuals with complex medication regimens should be prioritized for enhanced education. Furthermore, pharmacists collaborating with respiratory physicians to deprescribe and simplify dosage forms should be considered in the disease management process.

Rapid determination of toxic and essential metal binding proteins in biological samples by size exclusion chromatography-inductively coupled plasma tandem mass spectrometry.

Metalloproteins binding with trace elements play a crucial role in biological processes and on the contrary, those binding with exogenous heavy metals have adverse effects. However, the methods for rapid, high sensitivity and simultaneous analysis of these metalloproteins are still lacking. In this study, a fast method for simultaneously determination of both essential and toxic metal-containing proteins was developed by coupling size exclusion chromatography (SEC) with inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). After optimization of the separation and detection conditions, seven metalloproteins with different molecular weight (from 16.0 to 443.0 kDa) were successfully separated within 10 min and the proteins containing iron (Fe), copper (Cu), zinc (Zn), iodine (I) and lead (Pb) elements could be simultaneously detected with the use of oxygen as the collision gas in ICP-MS/MS. Accordingly, the linear relationship between log molecular weight and retention time was established to estimate the molecular weight of unknown proteins. Thus, the trace metal and toxic metal containing proteins could be detected in a single run with high sensitivity (detection limits in the range of 0.0020-2.5 µg/mL) and good repeatability (relative standard deviations lower than 4.5 %). This method was then successfully used to analyze metal (e.g., Pb, Zn, Cu and Fe) binding proteins in the blood of Pb-intoxicated patients, and the results showed a negative correlation between the contents of zinc and lead binding proteins, which was identified to contain hemoglobin subunit. In summary, this work provided a rapid and sensitive tool for screening metal containing proteins in large number of biological samples.

Heparan sulfate-dependent phase separation of CCL5 and its chemotactic activity.

Secreted chemokines form concentration gradients in target tissues to control migratory directions and patterns of immune cells in response to inflammatory stimulation; however, how the gradients are formed is much debated. Heparan sulfate (HS) binds to chemokines and modulates their activities. In this study, we investigated the roles of HS in the gradient formation and chemoattractant activity of CCL5 that is known to bind to HS. CCL5 and heparin underwent liquid-liquid phase separation and formed gradient, which was confirmed using CCL5 immobilized on heparin-beads. The biological implication of HS in CCL5 gradient formation was established in CHO-K1 (wild-type) and CHO-677 (lacking HS) cells by Transwell assay. The effect of HS on CCL5 chemoattractant activity was further proved by Transwell assay of human peripheral blood cells. Finally, peritoneal injection of the chemokines into mice showed reduced recruitment of inflammatory cells either by mutant CCL5 (lacking heparin-binding sequence) or by addition of heparin to wild-type CCL5. Our experimental data propose that co-phase separation of CCL5 with HS establishes a specific chemokine concentration gradient to trigger directional cell migration. The results warrant further investigation on other heparin-binding chemokines and allows for a more elaborate insight into disease process and new treatment strategies.

Construction of Highly Accurate Machine Learning Potential Energy Surfaces for Excited-State Dynamics Simulations Based on Low-Level Data Sets.

Machine learning is capable of effectively predicting the potential energies of molecules in the presence of high-quality data sets. Its application in the construction of ground- and excited-state potential energy surfaces is attractive to accelerate nonadiabatic molecular dynamics simulations of photochemical reactions. Because of the huge computational cost of excited-state electronic structure calculations, the construction of a high-quality data set becomes a bottleneck. In the present work, we first built two data sets. One was obtained from surface hopping dynamics simulations at the semiempirical OM2/MRCI level. Another was extracted from the dynamics trajectories at the CASSCF level, which was reported previously. The ground- and excited-state potential energy surfaces of ethylene-bridged azobenzene at the CASSCF computational level were constructed based on the former low-level data set. Although non-neural network machine learning methods can achieve good or modest performance during the training process, only neural network models provide reliable predictions on the latter external test data set. The BPNN and SchNet combined with the Δ-ML scheme and the force term in the loss functions are recommended for dynamics simulations. Then, we performed excited-state dynamics simulations of the photoisomerization of ethylene-bridged azobenzene on machine learning potential energy surfaces. Compared with the lifetimes of the first excited state (S1) estimated at different computational levels, our results on the E isomer are in good agreement with the high-level estimation. However, the overestimation of the Z isomer is unimproved. It suggests that smaller errors during the training process do not necessarily translate to more accurate predictions on high-level potential energies or better performance on nonadiabatic dynamics simulations, at least in the present case.

Tumor immune microenvironment remodeling and prognosis of patients with esophageal squamous cell carcinoma after neoadjuvant chemotherapy with and without immunotherapy: a retrospective cohort study.

Background: Immunochemotherapy was an emerging neoadjuvant treatment mode that can potentially benefit patients with esophageal carcinoma, but its synergistic mechanism and impact on the tumor immune microenvironment were still unclear. The purpose of this study was to investigate the outcomes of neoadjuvant chemotherapy (nCT) and neoadjuvant immunochemotherapy (nICT) in tumor microenvironment (TME) remodeling among patients with esophageal squamous cell carcinoma (ESCC) and to evaluate the prognostic value of immune-related biomarkers and clinicopathological characteristics. Methods: Patients with locally advanced ESCC who underwent neoadjuvant therapy followed by esophagectomy at the Fourth Hospital of Hebei Medical University between December 2019 and March 2022 were enrolled in this retrospective study. We examined TME features and immune antigen-related biomarkers before and after neoadjuvant therapy. Logistic and Cox regression model were used to evaluate the correlation between these factors and other clinical features and outcomes. Results: A total of 50 eligible participants were analyzed, including 31 males (62%), 25 patients of ≥65 years old, 4/28/18 of upper/middle/lower thoracic cancer, 25/17/8 of poor/moderate/high tumor differentiation, 8/42 of cT1+2/T3+4 stages and 30/20 of cN0/N+ stages. In the entire cohort, the rates of pathological complete response (pCR) and major pathological response (MPR) were 18% and 30%, respectively. pCR rates were 7.1% and 22.2% (χ2=0.699; P=0.40) MPR rates were 7.1% and 38.9% (χ2=4.837; P=0.03) in the nCT and nICT groups, respectively. Compared with the non-pCR patients, the pCR patients had a higher baseline programmed cell death ligand-1 (PD-L1) tumor proportion score (TPS) positive expression rate (16.7% vs. 77.8%, χ2=13.089; P<0.001). Following neoadjuvant therapy, the expression rates of PD-L1, CD3+ T cells, and CD8+ T cells in the tumor tissue was higher in the nICT group compared to the nCT group (P<0.05). Deficient expression of mismatch repair (MMR) genes was only observed in one patient (2%). Among patient-related biomarkers, lymphocyte and neutrophil counts decreased after treatment, with no significant changes in the neutrophil-to-lymphocyte ratio or platelet-to-lymphocyte ratio (PLR). Cox regression analysis showed that pretreatment, well-differentiated tumors and positive PD-L1 status were positive predictors of MPR (P<0.05). MPR was an independent predictor of disease-free survival (DFS) (P=0.03). Conclusions: Compared to nCT, nICT could more significantly upregulates PD-L1 TPS, PD-L1 combined positive score (CPS), CD3+ T cells, and CD8+ T cells. Pretreatment tumor differentiation and PD-L1 TPS level could be predictive of MPR. Our findings suggested that the combination of chemotherapy and immunotherapy may be more beneficial for activating anti-tumor immunity in the TME.

Mapping multimorbidity progression among 190 diseases.

BACKGROUND: Current clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them. METHODS: In this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations. RESULTS: We show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes. CONCLUSION: Our findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Mapping out clusters of diseases is crucial to addressing the rising challenge of co-occurrence of multiple diseases, known as multimorbidity. However, the current way of grouping diseases based on their associations isn't enough to understand how they develop over time. We've come up with a new approach to map out how groups of diseases progress together based on the strength of their causal relationships. By looking at how each disease affects the development of others, we can get a better understanding of how they form clusters. Our research goes beyond just showing which diseases occur together, and it's a step toward improving how we prevent and manage multiple health conditions in the future.

Triazine-structured covalent organic framework nanosheets with inherent hydrophilicity for the highly efficient and selective enrichment of glycosylated peptides.

Protein glycosylation plays a crucial role in various biological processes and is related to various diseases. Highly specific enrichment of glycopeptides before mass spectrometry detection is crucial for comprehensive glycoproteomic analysis. However, it still remains a great challenge due to the absence of affinity materials with excellent enrichment efficiency. In this work, a triazine structure linked by a -NH- bond of two-dimensional (2-D) covalent organic framework (COF) nanosheets was synthesized as an affinity adsorbent for the selective capture of glycopeptides. In particular, by introducing hydrophilic monomers via a bottom-up approach, the 2-D COF (denoted as NENP-1) nanosheets were provided with abundant amino groups and inherent hydrophilicity. Owing to the specific surface area and excessive accessible sites for hydrophilicity, the resulting NENP-1 nanosheets exhibited an outstanding glycopeptide enrichment efficiency from standard samples with a superior detection sensitivity (1 × 10-10 M), good enrichment selectivity (1 : 800, HRP tryptic digest to BSA protein), excellent binding capacity (100 mg g-1), great reusability, and recovery (60.2%). Furthermore, using the NENP-1 nanosheet adsorbent, twenty-four endogenous glycopeptides in the serum of patients with gastric cancer were successfully identified by LC-MS/MS technology, which illustrates a promising prospective of hydrophilic COF nanosheets in glycoproteomics research.

In Vivo Exposure Pathways of Ambient Magnetite Nanoparticles Revealed by Machine Learning-Aided Single-Particle Mass Spectrometry.

Nanosized ultrafine particles (UFPs) from natural and anthropogenic sources are widespread and pose serious health risks when inhaled by humans. However, tracing the inhaled UFPs in vivo is extremely difficult, and the distribution, translocation, and metabolism of UFPs remain unclear. Here, we report a label-free, machine learning-aided single-particle inductively coupled plasma mass spectrometry (spICP-MS) approach for tracing the exposure pathways of airborne magnetite nanoparticles (MNPs), including external emission sources, and distribution and translocation in vivo using a mouse model. Our results provide quantitative analysis of different metabolic pathways in mice exposed to MNPs, revealing that the spleen serves as the primary site for MNP metabolism (84.4%), followed by the liver (11.4%). The translocation of inhaled UFPs across different organs alters their particle size. This work provides novel insights into the in vivo fate of UFPs as well as a versatile and powerful platform for nanotoxicology and risk assessment.

Intestinal NSD2 Aggravates Nonalcoholic Steatohepatitis Through Histone Modifications.

Mounting clinical evidence suggests that a comprised intestinal barrier contributes to the progression of nonalcoholic steatohepatitis (NASH); nevertheless, the precise mechanism remains elusive. This study unveils a significant upregulation of nuclear receptor-binding SET domain protein 2 (NSD2) in the intestines of obese humans and mice subjected to a high-fat cholesterol diet (HFCD). Intestine-specific NSD2 knockout attenuated the progression of intestinal barrier impairment and NASH, whereas NSD2 overexpression exacerbated this progression. Mechanistically, NSD2 directly regulates the transcriptional activation of Ern1 by demethylating histone H3 at lysine 36 (H3K36me2), thus activating the ERN1-JNK axis to intensify intestinal barrier impairment and subsequently foster NASH progression. These findings elucidate the crucial role of NSD2-mediated H3K36me2 in intestinal barrier impairment, suggesting that targeting intestinal NSD2 can represent a novel therapeutic approach for NASH.