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Mitochondrial KMT9 methylates DLAT to control pyruvate dehydrogenase activity and prostate cancer growth
Abstract Prostate cancer (PCa) growth depends on de novo lipogenesis controlled by the mitochondrial pyruvate dehydrogenase complex (PDC). In this study, we identify lysine methyltransferase (KMT)9 as a regulator of PDC activity. KMT9 is localized in mitochondria of PCa cells, but not in mitochondria of other tumor cell types. Mitochondrial KMT9 regulates PDC activity by monomethylation of its subunit dihydrolipoamide transacetylase (DLAT) at lysine 596. Depletion of KMT9 compromises PDC activity, de novo lipogenesis, and PCa cell proliferation, both in vitro and in a PCa mouse model. Finally, in human patients, levels of mitochondrial KMT9 and DLAT K596me1 correlate with Gleason grade. Together, we present a mechanism of PDC regulation and an example of a histone methyltransferase with nuclear and mitochondrial functions. The dependency of PCa cells on mitochondrial KMT9 allows to develop therapeutic strategies to selectively fight PCa.
Signals of propaganda—Detecting and estimating political influences in information spread in social networks
Social networks are a battlefield for political propaganda. Protected by the anonymity of the internet, political actors use computational propaganda to influence the masses. Their methods include the use of synchronized or individual bots, multiple accounts operated by one social media management tool, or different manipulations of search engines and social network algorithms, all aiming to promote their ideology. While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. Propaganda in social networks is disguised as legitimate news sent from authentic users. It smartly blended real users with fake accounts. We seek here to detect efforts to manipulate the spread of information in social networks, by one of the fundamental macro-scale properties of rhetoric—repetitiveness. We use 16 data sets of a total size of 13 GB, 10 related to political topics and 6 related to non-political ones (large-scale disasters), each ranging from tens of thousands to a few million of tweets. We compare them and identify statistical and network properties that distinguish between these two types of information cascades. These features are based on both the repetition distribution of hashtags and the mentions of users, as well as the network structure. Together, they enable us to distinguish (p − value = 0.0001) between the two different classes of information cascades. In addition to constructing a bipartite graph connecting words and tweets to each cascade, we develop a quantitative measure and show how it can be used to distinguish between political and non-political discussions. Our method is indifferent to the cascade’s country of origin, language, or cultural background since it is only based on the statistical properties of repetitiveness and the word appearance in tweets bipartite network structures.
Sympathetic innervation induced by nerve growth factor promotes malignant transformation in gastric cancer
Valley charge-transfer insulator in twisted double bilayer WSe2
Tailoring anaesthetic strategies for diabetes research: Acepromazine vs. medetomidine in Aachen minipigs
Pre-established anaesthetic protocols in animal models might unexpectedly interfere with the main outcome of scientific projects and therefore they need to account for the specific research goals. We aimed to optimize the anaesthetic protocol and animal handling strategies in a diabetes-related-study exemplifying how the anaesthetic approach must be adjusted for individual research targets. Aachen minipigs were used as a model to test long-lasting skin glucose sensors for diabetic human patients. A total of 6 animals participated in two or three rounds of experiments. Each round lasted 2 months, with a maximum of 2 rounds per year. In each round, animals were anaesthetised 4 times: for glucose sensors insertion, twice for glucagon stress tests (GST), and a last time for removal of sensors. Acepromazine (ACE) was compared to medetomidine (MED) in association with butorphanol (BUT) and Ketamine (KET) and 4 parameters were analysed to define the optimum anaesthetic protocol including: sedation level, anaesthesia duration, effects on blood glucose and safety. ACE-BUT demonstrated a weaker sedative effect but reduced overall experimental time, minimized anaesthetic risk and minimally interfered with the glucose metabolism. The improvement obtained by animal conditioning and handling strategies applied in this study were not objectively estimated, although the aversion behavior was completely abolished. Based on the analysed parameters, the use of acepromazine is proposed to be superior when Aachen Minipigs are used specifically as a model for diabetes-related studies, albeit the recommendations for the anaesthesia of minipigs suggest otherwise.
Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms
Atmospheric wind energization of ocean weather
Abstract Ocean weather comprises vortical and straining mesoscale motions, which play fundamentally different roles in the ocean circulation and climate system. Vorticity determines the movement of major ocean currents and gyres. Strain contributes to frontogenesis and the deformation of water masses, driving much of the mixing and vertical transport in the upper ocean. While recent studies have shown that interactions with the atmosphere damp the ocean’s mesoscale vortices O (100) km in size, the effect of winds on straining motions remains unexplored. Here, we derive a theory for wind work on the ocean’s vorticity and strain. Using satellite and model data, we discover that wind damps strain and vorticity at an equal rate globally, and unveil striking asymmetries based on their polarity. Subtropical winds damp oceanic cyclones and energize anticyclones outside strong current regions, while subpolar winds have the opposite effect. A similar pattern emerges for oceanic strain, where subtropical convergent flow is damped along the west-equatorward east-poleward direction and energized along the east-equatorward west-poleward direction. These findings reveal energy pathways through which the atmosphere shapes ocean weather.
Anemia during pregnancy and adverse maternal outcomes in Georgia–A birth registry-based cohort study
Background Anemia in pregnancy is an important public health challenge; however, it has not been thoroughly studied in Georgia. We assessed the prevalence of anemia during pregnancy across Georgia and the association between anemia in the third trimester of pregnancy and adverse maternal outcomes. Methods We used data from the Georgian Birth Registry and included pregnant women who delivered between January 1, 2019, and August 31, 2022 (n = 158,668). The prevalence of anemia (hemoglobin (Hb) < 110 g/L) at any time during pregnancy was calculated per region. Pregnant women were classified into anemia severity groups based on their lowest measured Hb values, taking into account the thresholds for each trimester of pregnancy as defined by the WHO recommendations for anemia classification. Adjusted odds ratios (aOR) with 95% confidence intervals (CIs) were calculated for the associations between anemia status and post-delivery intensive care unit (ICU) admission and preterm delivery. Results The prevalence of anemia occurring at least once during pregnancy was 33.1%, with large regional differences in anemia prevalence (19.2%–32.8%). Of 105,811 pregnant women with Hb measurements in the third trimester, 71.0% had no anemia; 20.9%, mild anemia; and 8.1%, moderate or severe anemia. The odds of post-delivery ICU admission did not increase linearly with decreasing Hb value ( P for trend .13), and the relationship was inverse for preterm delivery ( P for trend .01). Conclusions A considerable proportion of pregnant women in Georgia have anemia during pregnancy, and the prevalence and quality of reporting differ across regions. Anemia occurring in the third trimester did not substantially increase the odds of maternal ICU admission or preterm delivery. To progress toward sustainable development goals and alleviate the public health burden of anemia, it is essential to not only identify and manage anemia during pregnancy but also address underlying factors with a multifaceted response.
Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification
Abstract The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification. This study introduces a revolutionary approach for accurately classifying diabetes, aiming to provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) is proposed in combination with Kernel Extreme Learning Machine (KELM) for a diabetes classification prediction model. First, the Secretary Bird Optimization Algorithm (SBOA) is enhanced by integrating a particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, and quantum computing-based t-distribution variations. The performance of QHSBOA is validated using the CEC2017 benchmark suite. Subsequently, QHSBOA is used to optimize the kernel penalty parameter $$\:C$$ and bandwidth $$\:c$$ of the KELM. Comparative experiments with other classification models are conducted on diabetes datasets. The experimental results indicate that the QHSBOA-KELM classification model outperforms other comparative models in four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, and specificity. This approach offers an effective method for the early diagnosis and prediction of diabetes.
Oligodendrocyte precursor cells facilitate neuronal lysosome release
Abstract Oligodendrocyte precursor cells (OPCs) shape brain function through many non-canonical regulatory mechanisms beyond myelination. Here we show that OPCs form contacts with their processes on neuronal somata in a neuronal activity-dependent manner. These contacts facilitate exocytosis of neuronal lysosomes. A reduction in the number or branching of OPCs reduces these contacts, which is associated with lysosome accumulation and altered metabolism in neurons and more senescent neurons with age. A similar reduction in OPC branching and neuronal lysosome accumulation is seen in an early-stage mouse model of Alzheimer’s disease. Our findings have implications for the prevention of age-related pathologies and the treatment of neurodegenerative diseases.
Pediatric Diabetic Ketoacidosis (PDKA) among newly diagnosed diabetic patients at Dilla University Hospital, Dilla, Ethiopia: Prevalence and predictors
Background Diabetic ketoacidosis (DKA) is a morbid complication of Type 1 diabetes mellitus(T1DM), and its occurrence at diagnosis has rarely been studied in Ethiopia, despite the many cases seen in the pediatric population. Objective The aim of this study was to know the prevalence of DKA among patients with newly diagnosed diabetes mellitus and identify avoidable risk factors. Method This institution-based retrospective cross-sectional study was conducted from December 1, 2018 to December1, 2022. Newly diagnosed T1DM under 15 years were included in the study. DKA and the new diagnosis of type 1 DM were defined based on the 2022 ISPAD and other international guidelines. A data collection form was used to collect sociodemographic and clinical data. Descriptive, bivariate, and multivariate logistic regression analyses were conducted to identify the risk factors. Result Among the 61 newly diagnosed T1DM pediatric patients admitted, DKA was the initial presentation in 37 patients, accounting for 60.7% of the cases. The mean age at diagnosis was 8 (±3.85) years, with females being more affected. Clinical presentation revealed vomiting accompanied by signs of dehydration (32.4%), with polyuria, polydipsia and weight loss (26.2%) being the most common symptoms. The presence of adequate knowledge of signs and symptoms of DM (AOR = 0.07, 95%CI 0.019–0.0897, P value 0.017) and a family history of DM (AOR = 0.129 95%CI 0.019–0.897, P value 0.039) were protective factors against DKA as the initial diagnosis of DM. Moreover, new-onset type 1 DM without DKA was 1.5 times higher in children from families with a high monthly income (AOR = 1.473, 95% CI 0.679–3.195 p value 0.000) compared to those from families with low income. The presence of an infection prior to DKA (AOR = 11.69,95%CI 1.34–10.1,P value 0.026) was associated with the diagnosis of DKA at the initial presentation of DM. Conclusion A high number of children present with diabetic ketoacidosis (DKA) at the initial diagnosis of diabetes mellitus (DM), which is associated with inadequate knowledge of the signs and symptoms of DM as well as the masking effect of concomitant infections in these children. Healthcare professionals should endeavor to suspect and screen children. Continuous awareness creation of DM is encouraged to diagnose diabetes mellitus earlier and to decrease the prevalence of DKA as an initial presentation.
Scrutinizing the evidence of anthracene toxicity on adrenergic receptor beta-2 and its bioremediation by fungal manganese peroxidase via in silico approaches
Upregulated FoxO1 promotes arrhythmogenesis in mice with heart failure and preserved ejection fraction
ISLRWR: A network diffusion algorithm for drug–target interactions prediction
Machine learning techniques and computer-aided methods are now widely used in the pre-discovery tasks of drug discovery, effectively improving the efficiency of drug development and reducing the workload and cost. In this study, we used multi-source heterogeneous network information to build a network model, learn the network topology through multiple network diffusion algorithms, and obtain compressed low-dimensional feature vectors for predicting drug–target interactions (DTIs). We applied the metropolis–hasting random walk (MHRW) algorithm to improve the performance of the random walk with restart (RWR) algorithm, forming the basis by which the self-loop probability of the current node is removed. Additionally, the propagation efficiency of the MHRW was improved using the improved metropolis–hasting random walk (IMRWR) algorithm, facilitating network deep sampling. Finally, we proposed a correction of the transfer probability of the entire network after increasing the self-loop rate of isolated nodes to form the ISLRWR algorithm. Notably, the ISLRWR algorithm improved the area under the receiver operating characteristic curve (AUROC) by 7.53 and 5.72%, and the area under the precision-recall curve (AUPRC) by 5.95 and 4.19% compared to the RWR and MHRW algorithms, respectively, in predicting DTIs performance. Moreover, after excluding the interference of homologous proteins (popular drugs or targets may lead to inflated prediction results), the ISLRWR algorithm still showed a significant performance improvement.
Application of mean maximum Young’s modulus value as a new parameter for differential diagnosis of prostate diseases
SEC-MX: an approach to systematically study the interplay between protein assembly states and phosphorylation
Abstract A protein’s molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Conducting SEC-MX on HEK293 and HCT116 cells, we generate a proof-of-concept dataset, mapping thousands of phosphopeptides and their assembly states. Our analysis reveals intricate relationships between phosphorylation events and assembly states and generates testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.
Effect of the intrinsic and extrinsic factors on the growth and development of young foals under subtropical conditions of Pakistan
This study was designed to explore the impact of intrinsic (breed of foal, age of dam, and age of foal at weaning) and extrinsic (season of birth and housing type) factors on the growth and survival of foals in the subtropical conditions of Pakistan. For the growth study, retrospective data analysis of foals (n = 150) born from purebred brood mares of Thoroughbred, Arabs, and Percheron breeds (n1, n2, and n3 = 50 each) was made. Six hundred and twenty-four (n = 624) foals born between 2020 to 2022 were observed for the study of foal survival rate. The survival of these foals till the age of one year was considered. To study the growth and development of foals, height, bone, and girth measurements were taken at multiple developmental stages (3, 6, 9, 12, 15, and 18 months of age). Statistical analysis revealed that late-weaned foals demonstrated superior growth metrics compared to early-weaned foals (P = 0.001) and sheltered housing conditions markedly enhanced growth parameters across all breeds and measurement intervals (P = 0.002). However, no significant effect of season (P > 0.05) on the growth measurements across breeds was found. Arab and Thoroughbred breeds demonstrated significant early growth advantages in foals from middle-aged dams, with marked differences in height, bone width, and girth; however, by 15 months, these differences were not statistically significant (P > 0.05). In contrast, Percheron foals showed consistent growth regardless of the dam’s age, suggesting breed-specific developmental influences (P = 0.885). Regarding the effects of extrinsic and intrinsic factors on foal survival, environmental conditions, and maternal age significantly impacted survival rates. Extreme winter conditions were associated with a notably lower survival probability (P = 0.002), and middle-aged dams exhibited significantly enhanced survival odds (P = 0.03). However, the influences of housing conditions and weaning age on survival were not statistically significant (P > 0.05), indicating these factors do not substantially affect foal survival within the first year. These results underscore the critical roles of weaning age, housing conditions, and age of dams in influencing foal growth and survival, highlighting the importance of tailored management practices in optimizing outcomes for the growth and development of young equines under subtropics.
Proteomic insights into dual-species biofilm formation of E. coli and E. faecalis on urinary catheters
Abstract Infections associated with urinary catheters are often caused by biofilms composed of various bacterial species that form on the catheters’ surfaces. In this study, we investigated the intricate interplay between Escherichia coli and Enterococcus faecalis during biofilm formation on urinary catheter segments using a dual-species culture model. We analyzed biofilm formation and global proteomic profiles to understand how these bacteria interact and adapt within a shared environment. Our findings demonstrated dynamic population shifts within the biofilms, with E. coli initially thriving in the presence of E. faecalis, then declining during biofilm development. E. faecalis exhibited a rapid decrease in cell numbers after 48 h in both single- and dual-species biofilms. Interestingly, the composition of the dual-species biofilms was remarkably diverse, with some predominantly composed of E. coli or of E. faecalis; others showed a balanced ratio of both species. Notably, elongated E. faecalis cells were observed in dual-species biofilms, a novel finding in mixed-species biofilm cultures. Proteomic analysis revealed distinct adaptive strategies E. coli and E. faecalis employed within biofilms. E. coli exhibited a more proactive response, emphasizing motility, transcription, and protein synthesis for biofilm establishment; whereas E. faecalis displayed a more reserved strategy, potentially downregulating metabolic activity, transcription, and translation in response to cohabitation with E. coli. Both E. coli and E. faecalis displayed significant downregulation of virulence-associated proteins when coexisting in dual-species biofilms. By delving deeper into these dynamics, we can gain a more comprehensive understanding of challenging biofilm-associated infections, paving the way for novel strategies to combat them.