Browse Articles

Ecological stoichiometry between leaves, litter and soil of dominant species in the forest community of rock-stream periglacial landforms in Mt. Laotudingzi

This study investigates the ecological stoichiometric characteristics of carbon (C), nitrogen (N), and phosphorus (P) across the leaf-litter-soil continuum in the block stream forest community of Laotudingzi Mountain, a representative paleo-periglacial landform in northeastern China. Utilizing X-ray fluorescence spectroscopy (XRF), we analyzed 13 dominant tree species (10 broadleaf, 3 coniferous) to unravel nutrient limitation mechanisms and cross-media coupling in this oligotrophic cryogenic ecosystem. Results indicate that P is the primary limiting nutrient, with mean N: P ratios in leaves (12.84), litter (11.25), and soil (8.05) below the global threshold for P limitation (N: P < 14). Cross-media stoichiometric feedbacks reveal efficient P cycling: leaf N: P ratios show significant positive correlations with litter P content (R = 0.557, P < 0.05) and negative correlations with litter C: P ratios (R = −0.581, P < 0.05), while synchronized P dynamics between litter and soil (R = 0.538, P < 0.1) underscore litter decomposition as the primary driver of soil P replenishment. Adaptive nutrient strategies emerge through stoichiometric plasticity, where accelerated P mineralization via low C: P ratios (342.88) compensates for C-N cycling inefficiencies despite inhibitory litter C: N ratios (32.3). These findings highlight the biogeochemical resilience of periglacial forests and provide critical insights for mitigating P limitation, guiding species selection in restoration, and optimizing litter management to enhance ecosystem stability under climate change.

SeeThrough: a rationally designed skull clearing technique for in vivo brain imaging

Expression of Concern: The IkappaB Kinase Family Phosphorylates the Parkinson’s Disease Kinase LRRK2 at Ser935 and Ser910 during Toll-Like Receptor Signaling

Catalytic refining lignin into toluene over atomically dispersed Cu/Ni dual sites

Correction: The role of maternal and child healthcare providers in identifying and supporting perinatal mental health disorders

Nascent liver proteome reveals enzymes and transcription regulators under physiological and alcohol exposure conditions

Abstract The liver proteome undergoes dynamic changes while performing hundreds of essential biological functions. Dysregulation of the liver proteome under alcoholic conditions leads to alcohol-associated liver disease (ALD), a major health challenge worldwide. There is an urgent need for quantitative and liver-specific proteome information in living animals to understand the pathophysiological dynamics of this largest solid organ. Here, we develop a comprehensive approach that specifically identifies the nascent proteome and preferentially enriches membrane proteins in living mouse hepatocytes and is broadly applicable to studies of the liver under various physiological and pathological conditions. In the ethanol-induced liver injury mouse model, the nascent proteome successfully identifies and validates a number of transcription regulators, enzymes, and protective chaperones involved in the molecular regulation of hepatic steatosis, in addition to almost all known regulatory proteins and pathways related to alcohol metabolism. We discover that Phb1/2 is an important transcription coregulator in the process of ethanol metabolism, and one identified fatty acid metabolism enzyme Acsl1/5, whose inhibition protects cells and mice from lipid accumulation, a key symptom of hepatic steatosis.

Comparative analysis of outcomes in high KDPI spectrum kidney transplants using unsupervised machine learning algorithm

Background The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98–100%) compared to those with moderately high KDPI scores (85–97%), employing a novel case-matching approach using machine learning. Methods We conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98–100% kidneys with recipients of KDPI 85–97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender. Results A total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98–100% cohort and 4.6 years for the KDPI 85–97% cohort. The five-year allograft survival was 51.7% for the KDPI 98–100% group versus 58% for the KDPI 85–97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56). Conclusion Transplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies.

Multi-omics analysis identifies an M-MDSC-like immunosuppressive phenotype in lineage-switched AML with KMT2A rearrangement

Abstract Lineage switching (LS) is the conversion of cancer cell lineage during the course of a disease. LS in leukemia cell lineage facilitates cancer cells escaping targeting strategy like CD19 targeted immunotherapy. However, the genetic and biological mechanisms underlying immune evasion by LS leukemia cells are not well understood. Here, we conduct a multi-omics analysis of patient samples and find that lineage-switched acute myeloid leukemia (LS AML) cells with KMT2A rearrangement (KMT2A-r) possess monocytic myeloid derived suppressor cell (M-MDSC)-like characteristics. Single-cell mass cytometry analysis reveals an increase in the M-MDSC like LS AML as compared to those of lineage-consistent KMT2A-r AML, and single-cell transcriptomics identify distinct expression patterns of immunoregulatory genes within this population. Furthermore, in vitro assays confirm the immunosuppressive capacity of LS AML cells against T cells, which is analogous to that of MDSCs. These data provide insight into the immunological aspects of the complex pathogenesis of LS AML, as well as development of future treatments.

Physical and mental health well-being of COVID-19 recovered patients: A phenomenological study

Aim This study aims to describe the experience of COVID-19 recovered patients’ physical and mental health well-being. Method A qualitative research approach was employed utilizing an unstructured interview protocol to explore the experiences of individuals recovering from COVID-19. Data were collected from 30 participants who had recovered from a moderate to severe form of the disease, all of whom required hospitalization with oxygen support during their illness. To gain an in-depth understanding of their post-recovery experiences, grand tour questions were used to facilitate open-ended discussions, allowing participants to fully articulate their perspectives on the impact of COVID-19 on their health and well-being. Results Thematic analysis of the interviews revealed four primary themes related to the post-recovery experiences of COVID-19 patients. Physical health concerns were widely reported, including persistent respiratory difficulties, joint and muscle pain, changes in activity levels, and the worsening of pre-existing health conditions. In terms of cognitive health, participants described experiencing memory loss, difficulty concentrating, and other cognitive impairments that affected their daily functioning. Psychological health challenges were also prominent, with many participants expressing feelings of anxiety, nervousness, loneliness, and sadness, reflecting the emotional toll of their illness and recovery. Additionally, sleep disturbances emerged as a significant issue, with individuals reporting difficulty falling asleep, fragmented sleep patterns, and persistent fatigue. These findings indicate that COVID-19 recovery extends beyond physical healing, affecting multiple aspects of an individual’s overall well-being. Conclusion This study highlights the extensive and multidimensional impact of COVID-19 recovery, affecting physical, cognitive, and psychological health, as well as sleep patterns. The persistence of symptoms such as respiratory issues, cognitive impairments, emotional distress, and sleep disturbances underscores the need for long-term medical and psychological support for recovered patients. These findings emphasize the importance of comprehensive post-recovery care, including rehabilitation programs, cognitive interventions, and mental health services to support individuals in regaining their overall well-being. Future research should focus on developing targeted interventions to address these long-term effects and improve the quality of life for COVID-19 survivors.

Predictive design of crystallographic chiral separation

Abstract The efficient separation of chiral molecules is a fundamental challenge in the manufacture of pharmaceuticals and light-polarising materials. We developed an approach that combines machine learning with a physics-based representation to predict resolving agents for chiral molecules, using a transformer-based neural network. In retrospective tests, our approach reaches a four to six-fold improvement over the historical - trial and error based - hit rate. We further validate the model in a prospective experiment, where we use the model to design a resolution screen for six unseen racemates. We successfully resolved three of the six mixtures in a single round of experiments and obtained an overall 8-to-1 true positive to false negative ratio. Together with this study, we release a previously proprietary dataset of over 6000 resolution experiments, the largest diastereomeric salt crystallisation dataset to date. More broadly, our approach and open crystallisation data lay the foundation for accelerating and reducing the costs of chiral resolutions.

Mediators of the stimulatory effect of S1P on colonic Na+/K+ ATPase

The Na ⁺ /K ⁺ ATPase, commonly known as the Na ⁺ /K⁺ pump, plays a crucial role in colonic sodium and water transport, inducing diarrhea or constipation. Inflammatory bowel disease is often associated with diarrhea and elevated levels of sphingosine-1-phosphate (S1P), suggesting a potential relationship between the pump and S1P. This study investigated the effects of S1P on colonic Na ⁺ /K ⁺ ATPase using Caco-2 cells as a model and the S1P analogue used in the treatment of multiple sclerosis, FTY720P. The pump activity was assessed by measuring the amount of inorganic phosphate released in the presence and absence of ouabain, an ATPase inhibitor. FTY720P induced an inhibition of Na ⁺ /K ⁺ ATPase at 15 minutes that was studied in a previous work. This inhibition shifted however, to stimulation at 2 hours, an effect that was abolished in the presence of JTE-013, an S1PR2 antagonist, and was replicated by CYM5520, an S1PR2 agonist. Further mechanistic exploration revealed that when PKC, NF-κB, COX, PKA, and PI3K enzymes were inhibited, FTY720P no longer influenced Na ⁺ /K ⁺ ATPase activity, indicating their involvement in the signaling cascade. Additional evidence supporting this pathway came from activators of these kinases and exogenous PGE₂, both of which stimulated the pump. The results indicate that FTY720P stimulates Na ⁺ /K ⁺ ATPase at 2 hours by binding to S1PR2, leading to PKC activation, followed by NF-κB-mediated induction of PGE₂ synthesis. PGE₂ then binds to its EP4 receptors, activating PKA and PI3K, ultimately resulting in an increase in the pump’s activity. These findings will open the door to targeted regulation of these intermediate molecules which could potentially alleviate certain undesirable effects of the drug.

Harnessing free energy calculations for kinome-wide selectivity in drug discovery campaigns with a Wee1 case study

Correlation between neutrophil extracellular traps and macrophages in thrombi of patients with acute ischemic stroke

Objective To investigate the correlation between Neutrophil Extracellular Traps (NETs) content and macrophages in thrombi of acute ischemic stroke (AIS) patients, as well as the differential degradation and clearance capacities of macrophages polarized into distinct functional states. Methods 60 AIS patients treated with endovascular mechanical thrombectomy at Bozhou People’s Hospital were enrolled. Thrombus samples from 30 patients underwent immunohistochemical staining for citrullinated histone 3 (CitH3), CD16, and CD163. CitH3-positive area percentage was quantified to evaluate NETs content. Pearson’s correlation analysis was applied to assess associations between M1(CD16⁺) and M2(CD163⁺) macrophage densities and the CitH3-positive area in thrombus. For the remaining 30 thrombi, co-culture experiments with polarized macrophages were conducted. CitH3 concentrations before and after co-culture were measured via enzyme-linked immunosorbent assay (ELISA), with a blank control group as a reference. Statistical comparisons between groups were performed using Student’s t-tests. Results All 30 thrombi exhibited positive expression of CitH3, CD16, and CD163. CD16+ and CD163+ macrophage densities significantly correlated with CitH3-positive area percentage (r = 0.538 and 0.641, P < 0.05). Co-culture with M1 or M2 macrophages significantly reduced CitH3 concentrations compared to the blank control (P < 0.05). Notably, M1 macrophages demonstrated superior NETs degradation efficacy compared to M2, as evidenced by lower post-co-culture CitH3 levels (P = 0.038). Conclusion NETs are contained in the thrombus of patients with acute ischemic stroke. The numbers of M1 macrophages and M2 macrophages in thrombus are positively correlated with the content of NETs. M1 and M2 macrophages derived from human monocytes have the ability to degrade and clear NETs, and the effect of M1 macrophages in degrading and clearing NETs may be stronger than M2 macrophages.

Crafting defects in two-dimensional organic platelets via seeded coassembly enables emergent molecular recognition

Covert communication performance evaluation in UAV-assisted rate-splitting multiple access systems

In this paper, we investigate using rate-splitting multiple access (RSMA) to facilitate covert communication in a multi-user unmanned aerial vehicle (UAV) downlink communication network that is being monitored by a warden (Willie). We establish a comprehensive analytical framework and derive closed-form expressions for key performance metrics under Nakagami-m fading channels. Specifically, we analyze the detection error probability (DEP) at Willie to quantify system covertness, in addition to outage probability (OP) and ergodic rate (ER) experienced by legitimate users, along with asymptotic analysis in the high signal-to-noise ratio (SNR) region. Furthermore, we propose an efficient alternating optimization algorithm to determine the optimal static position of the UAV that maximizes system covertness. Numerical simulations support the theoretical results derived, present the impact of various system parameters, and provide a performance comparison with non-orthogonal multiple access (NOMA). Results indicate that RSMA offers significant covertness gains over the NOMA scheme.

Sex-stratified genome-wide association meta-analysis of major depressive disorder

Abstract There are striking sex differences in the prevalence and symptomology of Major Depressive Disorder. Here, we conduct the largest sex-stratified genome wide association and genotype-by-sex interaction meta-analyses of Major Depressive Disorder to date (Females: 130,471 cases, 159,521 controls. Males: 64,805 cases, 132,185 controls). We identify 16 and eight independent genome-wide significant variants in females and males, respectively, including one novel variant on the X chromosome. Major Depressive Disorder in females and males shows substantial genetic overlap with a large proportion of variants displaying similar effect sizes across sexes. However, we also provide evidence for a higher burden of genetic risk in females which could be due to female-specific variants. Additionally, sex-specific pleiotropic effects may contribute to the higher prevalence of metabolic symptoms in females with Major Depressive Disorder. These findings underscore the importance of considering sex-specific genetic architectures in the study of health conditions, including Major Depressive Disorder, paving the way for more targeted treatment strategies.

The complex interplay between psychological factors and sports performance: A systematic review and meta-analysis

The increasing body of research underscores that athletic performance is not solely contingent upon physical capabilities but is also significantly influenced by psychological strengths. Despite this, there remains a need for comprehensive meta-analyses to rigorously investigate the link between psychological factors and sports performance. In line with this, the present study seeks to examine the influence of various psychological constructs, such as motivation, self-efficacy, self-confidence, goal setting, attention, stress management, extraversion, self-discipline, personality traits, and emotional intelligence (EI) on athletic performance. This analysis was conducted in accordance with PRISMA guidelines and included 127 studies covering a total of 24,358 participants. The methodological quality of the included studies was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist, ensuring a rigorous evaluation of study designs and data reliability. The findings indicate an association between personality traits—such as motivation (d = 0.525), self-efficacy (d = 0.413), conscientiousness (d = 0.316), and extraversion (d = 0.336)—and sports performance. Moreover, the overall association between psychological factors and sports performance was calculated as moderate (d = 0.329). Moderator analyses revealed no significant associations based on variables such as gender, type of sport, or type of athlete. Additionally, no significant associations were found for anxiety, openness to experience, neuroticism, or agreeableness, suggesting that these traits may have more complex or context-dependent relationships with performance. The findings of this meta-analysis indicate that psychological skills training plays a critical role in enhancing athletes’ performance. Future research should delve deeper into studies conducted in specific contexts to better understand the ambivalent relations among these factors.

Optogenetic actin network assembly on lipid bilayer uncovers the network density-dependent functions of actin-binding proteins

Enhancing the early detection of Alzheimer’s disease using an integrated CNN-LSTM framework: A robust approach for fMRI-based multi-stage classification

Alzheimer’s Disease poses a significant challenge as a progressive and irreversible neurological condition striking the elderly population. Its incurable nature correlates with a significant rise in death rates. However, early detection can slow its progression and facilitate prompt intervention, thereby mitigating mortality risks. Functional Magnetic Resonance Imaging (fMRI) provides valuable insights into the functional changes within distinct brain regions associated with the disease. The recent research efforts have extracted functional connectivity measures for the classification. These handcrafted functional connectivity features are usually not robust and are computationally intensive. To address the issue, this study introduces an integrated deep-learning framework based on CNN and LSTM networks. This framework autonomously learns both intra-volume and inter-volume features critical for classification tasks. CNNs facilitate feature extraction, while LSTM networks govern the selection of significant features for classification. The key aim of this study is to classify Alzheimer’s disease and its prodromal stage, Mild Cognitive Impairment (MCI). MCI is further categorized as early MCI (EMCI) and late MCI (LMCI). We have evaluated the framework in three dimensions, binary classification, multi-class classification with 3-classes, and multi-class classification with 4-classes. For each dimension, multiple classifications were performed. The results depict the proposed CNN-LSTM framework to attain 99% accuracy and 100% average area under the curve for the majority of the classification.

Peripheral blood DNA methylation predicts the early onset of primary tumor in TP53 mutation carriers