Browse Articles

The CYLD–PARP1 feedback loop regulates DNA damage repair and chemosensitivity in breast cancer cells

Poly(ADP-ribose) polymerase 1 (PARP1) plays a crucial role in DNA repair and genomic stability maintenance. However, the regulatory mechanisms governing PARP1 activity, particularly through deubiquitination, remain poorly elucidated. Using a deubiquitinase (DUB) library binding screen, we identified cylindromatosis (CYLD) as a bona fide DUB for PARP1 in breast cancer cells. Mechanistically, CYLD is recruited by PARP1 to DNA lesions upon genotoxic stress, where it cleaves K63-linked polyubiquitin chains on PARP1 at residues K748, K940, and K949, resulting in compromised PARP1 activation. In a reciprocal manner, PARP1 PARylates CYLD at sites E191, E231, E259, and E509, thereby enhancing its DUB activity. Consequently, depletion of CYLD leads to increased efficiency in base excision repair and confers breast cancer cells with resistance to alkylating agents. Conversely, overexpression of CYLD enhances sensitivity to PARP inhibitors (PARPi) even in homologous recombination-proficient breast cancer cells. These findings offer unique insights into the intricate interplay between CYLD and PARP1 in DNA repair, underscoring the pivotal role of targeting this regulatory axis for breast cancer chemotherapy.

Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers seek a model capable of forecasting energy consumption both in the short term and the long term. Traditional models for energy prediction focus on either short-term or long-term accuracy, often failing to optimize both simultaneously. Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. CNNs discover patterns in data, LSTM identifies long-term dependencies and sequential patterns and Bi-LSTM identifies complex temporal relations within the data. Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions.

Vulnerable parafoveal microcirculation quadrant in patients with type 2 diabetes mellitus

RAP-2 and CNH-MAP4 Kinase MIG-15 confer resistance in bystander epithelium to cell-fate transformation by excess Ras or Notch activity

Induction of cell fates by growth factors impacts many facets of developmental biology and disease. LIN-3/EGF induces the equipotent vulval precursor cells (VPCs) in Caenorhabditis elegans to assume the 3˚−3˚−2˚−1˚−2˚−3˚ pattern of cell fates. 1˚ and 2˚ cells become specialized epithelia and undergo stereotyped series of cell divisions to form the vulva. Conversely, 3˚ cells are relatively quiescent and nonspecialized; they divide once and fuse with the surrounding epithelium. 3˚ cells have thus been characterized as passive, uninduced, or ground state. Based on our previous studies, we hypothesized that a 3˚-promoting program would confer resistance to cell fate-transformation by inappropriately activated 1˚ and 2˚ fate-promoting LET-60/Ras and LIN-12/Notch, respectively. Deficient MIG-15/CNH-MAP4 Kinase meets the expectations of genetic interactions for a 3˚-promoting protein. Moreover, endogenous MIG-15 is required for expression of a fluorescent biomarker of 3˚ cell fate, is expressed in VPCs, and functions cell autonomously in VPCs. The Ras family small GTPase RAP-2, orthologs of which activate orthologs of MIG-15 in other systems, emulates these functions of MIG-15. However, gain of RAP-2 function has no effect on patterning, suggesting its activity is constitutive in VPCs. The 3˚ biomarker is expressed independently of the AC, raising questions about the cellular origin of 3˚-promoting activity. Activated LET-60/Ras and LIN-12/Notch repress expression of the 3˚ biomarker, suggesting that the 3˚-promoting program is both antagonized by as well as antagonizes 1˚- and 2˚- promoting programs. This study provides insight into developmental properties of cells historically considered to be nonresponding to growth factor signals.

An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia

Background Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital mortality in VAP patients. This study aims to develop an interpretable machine learning (ML) prediction model to enhance the assessment of in-hospital mortality risk in VAP patients. Methods This study extracted VAP patient data from versions 2.2 and 3.1 of the MIMIC-IV database, using version 2.2 for model training and validation, and version 3.1 for external testing. Feature selection was conducted using the Boruta algorithm, and 14 ML models were constructed. The optimal model was identified based on the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity across both validation and test cohorts. SHapley Additive exPlanations (SHAP) analysis was applied for global and local interpretability. Results A total of 1,894 VAP patients were included, with 12 features ultimately selected for model construction: 24-hour urine output, blood urea nitrogen, age, diastolic blood pressure, platelet count, anion gap, body temperature, bicarbonate level, sodium level, body mass index, and whether combined with congestive heart failure and cerebrovascular disease. The random forest (RF) model showed the best performance, achieving an AUC of 0.780 in internal validation and 0.724 in external testing, outperforming other ML models and common clinical scoring systems. Conclusion The RF model demonstrated robust and reliable performance in predicting in-hospital mortality risk for VAP patients. The developed online tool can assist clinicians in efficiently assessing VAP in-hospital mortality risk, supporting clinical decision-making.

Risk factors for all-cause mortality during the COVID-19 pandemic compared with the pre-pandemic period in an adult population of Arkhangelsk, Russia

Abstract We investigated and compared mortality rates and risk factors for pre-pandemic and pandemic all-cause mortality in a population-based cohort of men and women in Arkhangelsk, Russia. A prospective cohort study enrolled 2,324 participants aged 35 to 69 years between 2015 and 2017. All participants were followed up for all-cause deaths using the mortality registry. Mortality rates per 1000 person-years were calculated for men and women in the pre-pandemic and pandemic periods. Cox regression models were used to investigate demographic, lifestyle, and health characteristics associated with increased risk of death in both periods. During the pandemic, age-standardized all-cause mortality increased in women, but minor change was observed in men. Older age, smoking, and diabetes were associated with a higher risk of all-cause death in both periods and for both sexes. In women, higher risk during the pandemic was associated with obesity, angina, elevated cystatin C levels, and a history of COVID-19. In men, asthma and elevated hs-Troponin T levels increased the risk of death during the pandemic, while elevated hs-CRP and NT-proBNP levels were associated with higher risk in both periods. Targeted preventive interventions for men and women with specific risk factors can be implemented during potential future infectious disease outbreaks.

Elucidation of a distinct photoreduction pathway in class II <i>Arabidopsis thaliana</i> photolyase

Class II photolyases (PLs) are a distant subclade in the photolyase/cryptochrome superfamily, displaying a unique Trp–Tyr tetrad for photoreduction and exhibiting a lower quantum yield (QY) of DNA repair (49%) than class I photolyases (82%) [M. Zhang, L. Wang, S. Shu, A. Sancar, D. Zhong, Science 354 , 209–213 (2016)]. Using layer-by-layer mutant design and femtosecond spectroscopy, we have successfully determined the rates of electron transfer and proton transfer, driving force, and reorganization energy for nine elementary steps involved in the initial photoreduction of class II Arabidopsis thaliana photolyase (AtPL), thereby constructing the photoreduction network specific to class II PLs. Several dynamic features have been revealed including a slow-rise (172 ps) and fast-decay (26 ps) kinetics between the excited lumiflavin and adenine groups within the flavin adenine dinucleotide cofactor, a slower electron transfer (ET) (22 ps) between the excited lumiflavin and the nearest Trp in the Trp triad (W a ) as compared to reported class I PL (0.8 ps), and a rapid deprotonation of the distal Trp in the Trp triad (W c ). Most strikingly, we captured a slightly energetically unfavorable ET step between W a and the center Trp (W b ), as opposed to the decreasing reduction potential observed in class I PL that drives the electron flow unidirectionally. Such an energetically uphill ET step leads to a lower photoreduction quantum yield (~34%) in class II AtPL compared to that of class I PL (~45%), raising an important question on the evolutionary implications of various photoreduction networks in photolyases and cryptochromes.

Perceptions of access to harm reduction services during the COVID-19 pandemic among people who inject drugs in metropolitan Chicago

Background The COVID-19 pandemic amplified the risk environment for people who inject drugs (PWID), making continued access to harm reduction services imperative. Research has shown that some harm reduction service providers were able to continue to provide services throughout the pandemic. Most of these studies, however, focused on staff perspectives, not those of PWID. Our study examines changes in perceptions of access to harm reduction services (e.g., participant reported difficulty in accessing syringes and naloxone) among PWID participating in a longitudinal study conducted through the University of Illinois-Chicago’s Community Outreach Intervention Projects field sites during the COVID-19 pandemic. Methods A COIVD-19 survey module was administered from March 2020-February 2022 to participants of an ongoing longitudinal study of PWID ages 18–30, who were English-speaking, and were residing in the Chicago Metropolitan Area. Responses to the COVID-19 survey module were analyzed to understand how study participants’ self-reported access to harm reduction services changed throughout the pandemic. Baseline responses to the survey were analyzed to compare participant-reported drug use behaviors and perceived access to harm reduction services across COIVD-19 time periods. Mixed effects logistic regression was used to examine difficulty in syringe access as an outcome of COVID-19 time period. Results Participants had significantly lower odds (AOR = 0.28; 95% CI 0.12–0.65) of reporting difficulty in accessing syringes later in the pandemic. However, the majority of participants reported access to syringes and naloxone remained the same as before the pandemic. Conclusions The lack of perceived changes in harm reduction access by PWID and the decrease in those reporting difficulty accessing syringes as the pandemic progressed suggests the efficacy of adaptations to harm reduction service provision (e.g., window and mobile service) during the pandemic. Further research is needed to understand how the COVID-19 pandemic may have impacted PWIDs’ engagement with harm reduction services.

Effects of exosomes from human dental pulp stem cells on the biological behavior of human fibroblasts

Higher-order transient membrane protein structures

This study shows that five membrane proteins—three GPCRs, an ion channel, and an enzyme—form self-clusters under natural expression levels in a cardiac-derived cell line. The cluster size distributions imply that these proteins self-oligomerize reversibly through weak interactions. When the concentration of the proteins is increased through heterologous expression, the cluster size distributions approach a critical distribution at which point a phase transition occurs, yielding larger bulk phase clusters. A thermodynamic model like that explaining micellization of amphiphiles and lipid membrane formation accounts for this behavior. We propose that many membrane proteins exist as oligomers that form through weak interactions, which we call higher-order transient structures (HOTS). The key characteristics of HOTS are transience, molecular specificity, and a monotonically decreasing size distribution that may become critical at high concentrations. Because molecular specificity invokes self-recognition through protein sequence and structure, we propose that HOTS are genetically encoded supramolecular units.

Barriers and enablers to opioid deprescription: A qualitative study

Background Canada has the fourth highest per capita rate of opioid prescriptions in the world, contributing to the country’s opioid crisis. Due to both their pain-relieving and euphoric properties, opioids can be highly addictive, leading to potential overdose and death. Deprescription is an endorsed and organized method of discontinuing a drug but very little is known about the barriers that Canadian physicians face when attempting to deprescribe opioids, particularly those who practice in rural areas (which have some of the highest rates of opioid users). Methods This was an explorative, qualitative study describing rural family doctors’ experiences and practices regarding opioid deprescription in primary care. A convenience sample of family doctors who had experience working with patients taking opioid medications was recruited from the professional networks of study team members. After consenting to participate, data was collected using semi-structured telephone interviews and analyzed by researchers experienced in applying the Theoretical Domains Framework to assess barriers and enablers of behavior change. Principal findings 10 physicians participated in this study. Our analysis revealed four barriers and five enablers related to opioid deprescription in rural primary care. Barriers include a lack of knowledge and skills related to deprescribing, discomfort initiating deprescription, patient pressure to continue prescribing opioids, and a lack of foundational support required to deprescribe. Enablers include working with colleagues who share common views on overuse of opioids and deprescription; access to other healthcare providers, community-based resources, and clinical tools; using a systematic approach to deprescription; previous experience successfully deprescribing opioids; and practicing in a rural setting. Conclusions Opioid dependence and over-prescription continue to be a problem for our health system. Deprescription is necessary but challenging for family physicians. Rural physicians are keenly aware of the importance of preserving the physician-patient therapeutic relationship and open and clear communication about opioid medications and deprescription but feel unprepared to manage this in the face of difficult issues surrounding deprescription. They also feel unprepared to deal with deprescription effectively without access to other resources, healthcare professionals, patient education materials and time. Rural physicians would benefit most from added foundational supports for deprescription.

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images

Predicting gene sequences with AI to study codon usage patterns

Selective pressure acts on the codon use, optimizing multiple, overlapping signals that are only partially understood. We trained AI models to predict codons given their amino acid sequence in the eukaryotes Saccharomyces cerevisiae and Schizosaccharomyces pombe and the bacteria Escherichia coli and Bacillus subtilis to study the extent to which we can learn patterns in naturally occurring codons to improve predictions. We trained our models on a subset of the proteins and evaluated their predictions on large, separate sets of proteins of varying lengths and expression levels. Our models significantly outperformed naïve frequency-based approaches, demonstrating that there are learnable dependencies in evolutionary-selected codon usage. The prediction accuracy advantage of our models is greater for highly expressed genes and is greater in bacteria than eukaryotes, supporting the hypothesis that there is a monotonic relationship between selective pressure for complex codon patterns and effective population size. In S . cerevisiae and bacteria, our models were more accurate for longer proteins, suggesting that the learned patterns may be related to cotranslational folding. Gene functionality and conservation were also important determinants that affect the performance of our models. Finally, we showed that using information encoded in homologous proteins has only a minor effect on prediction accuracy, perhaps due to complex codon-usage codes in genes undergoing rapid evolution. Our study employing contemporary AI methods offers a unique perspective and a deep-learning-based prediction tool for evolutionary-selected codons. We hope that these can be useful to optimize codon usage in endogenous and heterologous proteins.

APOE4 and infectious diseases jointly contribute to brain glucose hypometabolism, a biomarker of Alzheimer’s pathology: New findings from the ADNI

Background Impaired brain glucose metabolism is a preclinical feature of neurodegenerative diseases such as Alzheimer’s disease (AD). Infections may promote AD-related pathology. Therefore, we investigated the interplay between infections and APOE4, a strong genetic risk factor for AD. Methods We analyzed data on 1,509 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using multivariate linear regression models. The outcomes were rank-normalized hypometabolic convergence index (HCI), statistical regions of interest (SROI) for AD, and mild cognitive impairment (MCI). Marginal mean estimates for infections, stratified by APOE4 carrier status, were then computed. Results Prior infections were associated with greater HCI [β = 0.15, 95% CI: 0.03, 0.27, p = 0.01]. The combined effects of infections and APOE4 carriers on HCI levels were significantly greater than either variable alone. Among APOE4 carriers, the estimated marginal mean was 0.62, rising to 0.77, with infections (p&lt;0.001), indicating an interaction effect. Carriers with multiple infections showed greater hypometabolism (higher HCI), with an estimate of 0.44 (p = 0.01) compared to 0.11 (p = 0.08) for those with a single infection, revealing a dose-response relationship. The estimates for the association of infections with SROI AD and SROI MCI were β = -0.01 (p = 0.02) and β = -0.01 (p = 0.04), respectively. Conclusion Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a “multi-hit” mechanism in AD development.

Patterns and implications of spatial covariation in herbivore functions on resilience of coral reefs

Abstract Persistent shifts to undesired ecological states, such as shifts from coral to macroalgae, are becoming more common. This highlights the need to understand processes that can help restore affected ecosystems. Herbivory on coral reefs is widely recognized as a key interaction that can keep macroalgae from outcompeting coral. Most attention has been on the role ‘grazing’ herbivores play in preventing the establishment of macroalgae, while less research has focused on the role of ‘browsers’ in extirpating macroalgae. Here we explored patterns, environmental correlates and state shift consequences of spatial co-variation in grazing and browsing functions of herbivorous fishes. Grazing and browsing rates were not highly correlated across 20 lagoon sites in Moorea, French Polynesia, but did cluster into 3 (of 4) combinations of high and low consumption rates (no site had low grazing but high browsing). Consumption rates were not correlated with grazer or browser fish biomass, but both were predicted by specific environmental variables. Experiments revealed that reversibility of a macroalgal state shift was strongly related to spatial variation in browsing intensity. Our findings provide insights and simple diagnostic tools regarding heterogeneity in top-down forcing that influences the vulnerability to and reversibility of shifts to macroalgae on coral reefs.

Topological confinement by a membrane anchor suppresses phase separation into protein aggregates: Implications for prion diseases

Protein misfolding and aggregation are a hallmark of various neurodegenerative disorders. However, the underlying mechanisms driving protein misfolding in the cellular context are incompletely understood. Here, we show that the two-dimensional confinement imposed by a membrane anchor stabilizes the native protein conformation and suppresses liquid–liquid phase separation (LLPS) and protein aggregation. Inherited prion diseases in humans and neurodegeneration in transgenic mice are linked to the expression of anchorless prion protein (PrP), suggesting that the C-terminal glycosylphosphatidylinositol (GPI) anchor of native PrP impedes spontaneous formation of neurotoxic and infectious PrP species. Combining unique in vitro and in vivo approaches, we demonstrate that anchoring to membranes prevents LLPS and spontaneous aggregation of PrP. Upon release from the membrane, PrP undergoes a conformational transition to detergent-insoluble aggregates. Our study demonstrates an essential role of the GPI anchor in preventing spontaneous misfolding of PrP C and provides a mechanistic basis for inherited prion diseases associated with anchorless PrP.

Deep learning-based differential gut flora for prediction of Parkinson’s

Background There had been extensive research on the role of the gut microbiota in human health and disease. Increasing evidence suggested that the gut-brain axis played a crucial role in Parkinson’s disease, with changes in the gut microbiota speculated to be involved in the pathogenesis of Parkinson’s disease or interfere with its treatment. However, studies utilizing deep learning methods to predict Parkinson’s disease through the gut microbiota were still limited. Therefore, the goal of this study was to develop an efficient and accurate prediction method based on deep learning by thoroughly analyzing gut microbiota data to achieve the diagnosis of Parkinson’s disease. Methods This study proposed a method for predicting Parkinson’s disease using differential gut microbiota, named the Parkinson Gut Prediction Method (PGPM). Initially, differential gut microbiota data were extracted from 39 Parkinson’s disease (PD) patients and their corresponding 39 healthy spouses. Subsequently, a preprocessing method called CRFS (combined ranking using random forest scores and principal component analysis contributions) was introduced for feature selection. Following this, the proposed LSIM (LSTM-penultimate to SVM Input Method) approach was utilized for classifying Parkinson’s patients. Finally, a soft voting mechanism was employed to predict Parkinson’s disease patients. Results The research results demonstrated that the Parkinson gut prediction method (PGPM), which utilized differential gut microbiota, performed excellently. The method achieved a mean accuracy (ACC) of 0.85, an area under the curve (AUC) of 0.92, and a receiver operating characteristic (ROC) score of 0.92. Conclusion In summary, this method demonstrated excellent performance in predicting Parkinson’s disease, allowing for more accurate predictions of Parkinson’s disease.

Enhancing chickpea yield through the application of sulfur and sulfur-oxidizing bacteria

Quantifying the genetic origins of body plan scaling

Effects of pre-oxidation temperature and air volume on oxidation thermogravimetric and functional group change of lignite

To investigate the impact of the oxidation temperature and variations in airflow conditions on coal spontaneous combustion characteristics, pre-oxidized coal samples were prepared using a programmed temperature rise method. Synchronous thermal analysis experiments and Fourier transform infrared spectroscopy were conducted to explore changes in the thermal effects and functional group content of the coal samples, respectively. The results indicate that variations in pre-oxidation conditions primarily in fluence the activation temperature and maximum weight loss temperature of the coal samples, while exerting a lesser impact on the critical temperature and ignition point. Variations in air volume conditions predominantly affect the content of Ar-C-O- and -CH2 &amp; -CH3 in the oxygen-containing functional group region. The trend of the average activation energy within a conversion rate range of 0.2 to 0.6 of pre-oxidized coal samples changing with the increased of pre-oxidation temperature under the air flow conditions of 25mL/min and 50mL/min is consistent, but opposite to that under the air flow conditions of 100mL/min and 200mL/min. Compared to raw coal, under an airflow rate of 50 mL/min and when oxidized to 110°C, the coal sample exhibits an increase in the content of OH…OH, accompanied by reductions in the critical temperature, activation temperature, ignition point, and maximum weight loss temperature to varying degrees, thereby rendering it more susceptible to oxidative spontaneous combustion.