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Rational design of novel Plasmodium falciparum glutamyl-tRNA synthetase inhibitors for the development of next-generation antimalarial drugs

A major obstacle in treating malaria is the emergence of antimalarial drug resistance. The aminoacyl-tRNA synthetases (aaRSs), which are essential enzymes for protein synthesis, have been identified as potential targets for the discovery of anti-parasitic drugs. Of these, Glutamyl-tRNA synthetase (GluRS) is a crucial aaRS enzyme in Plasmodium falciparum and is a vital therapeutic target. In this study, potential GluRS inhibitors are reported from the Medicinal Fungi Secondary Metabolites and Therapeutics (MeFSAT) chemical library using structure based virtual screening via PyRx v0.8. To evaluate the stability of top hit protein-inhibitor complexes, molecular dynamics simulations (time period, 100 ns) were performed from an initial batch of 1,830 compounds. The two shortlisted compounds, MSID000152 and MSID000974, showed the strongest negative binding affinities of −10.4 kcal/mol and −10.1 kcal/mol, respectively. For comparative analysis, Chloroquine was used as a control to validate the screening results. The dynamics of both complexes showed stable structure, with mean RMSD of 3.94 Å for MSID000152, 2.35 Å for MSID000974, and 2.39 Å for chloroquine. The MSID000152 illustrated favorable MMPB/GBSA binding free energy. Taken together, the study highlights MSID000152 and MSID000974 as promising antimalarial lead compounds for additional in vitro and in vivo investigations.

Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema

Information about task duration influences energetic cost during split-belt adaptation and retention of walking patterns post-adaptation

Studies of locomotor adaptation have shown that adaptation can occur in short bouts and can continue for long bouts or across days. Information about task duration might influence the adaptation of gait features, given that task duration influences the time available to explore and adapt the aspects of gait that reduce energy cost. We hypothesized that information about task duration and frequency of updates influences adaptation to split-belt walking based on two competing mechanisms: individuals anticipating a prolonged adaptation period may either (1) extend exploration of energetically suboptimal gait patterns, or (2) adapt toward a more energy-efficient pattern earlier to maintain an energetic reserve. We tested three groups: N = 19 participants received minute-by-minute updates during a 10-minute adaptation duration (True group), N = 19 participants received no updates during a 10-minute adaptation duration and were misled to expect a prolonged 30-minute adaptation duration (False group), and N = 14 participants received one update halfway through a 10-minute adaptation duration (Control group). We measured step length asymmetry, leg work, and metabolic cost. Our results partially supported our hypothesis but did not confirm the underlying mechanisms. While step length asymmetry did not differ significantly between groups during adaptation, the True group generated a more effortful gait pattern with a greater increase in metabolic cost and higher work with the slow leg. Additionally, the True group showed no association between the different adapted gait variables such as step length asymmetry and metabolic cost, contrary to the Control and False groups. Finally, we observed that the False group showed greater retention of the split-belt aftereffects than the Control and False groups. Thus, adapted locomotor and energetic patterns are influenced by information about task duration, indicating that Information about task duration should be controlled for, or can be manipulated to elicit different efforts during adaptation.

Memes reveal threats to graduate-student mental health

Gene expression profiling and predictive modeling of cancer biomarkers using machine learning and IoT-Enabled biosensors

Abnormal brain network reconfiguration in neuropsychiatric disorders across cognitive decline, Depression, and Schizophrenia

Objective Neuropsychiatric disorders are characterized by high complexity and comorbidity, imposing a substantial burden on both patients and society. However, their elusive pathogenic mechanisms impede accurate clinical diagnosis and effective interventions. To overcome this challenge, the present study proposes a novel framework to quantify and characterize these disorders. Methods Routine electroencephalogram (EEG) recordings are acquired from 236 subjects, including patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI), major depressive disorder (MDD), schizophrenia, and healthy controls (HCs). Time-varying functional brain networks are constructed by phase locking value (PLV) analysis on band-pass filtered EEG signals. Subsequently, the nodal behavior characteristics within these dynamic brain networks are quantified by integrating robust dynamic community detection algorithms and network reconfiguration metrics. Results Significant intergroup differences in network reconfiguration metrics are identified based on the dynamic community structures (FDR-corrected p < 0.001 ). Lower cohesion strength is observed across all neuropsychiatric disorders compared to healthy controls, consistent across all frequency bands and recording sites. When six machine learning classifiers are trained on these metrics, the maximum classification accuracies exceeded 80%. Since lower cohesion strength is a prominent potential biomarker for neuropsychiatric disorders, it was then selected as the independent input feature for random forest classifier, and the classification accuracy achieved 0.85 for schizophrenia group, 0.88 for both the MCI and MDD group, and 0.82 for the AD group. Conclusions Our findings indicate that the framework based on dynamic network reconfiguration metrics effectively captures both the shared and disorder-specific alterations in brain network dynamics among neuropsychiatric disorders. Significance Dynamic community structure advances our understanding of the pathological mechanisms underlying neuropsychiatric disorders. This study provides novel insights that may inform the development of more targeted and effective therapeutic strategies.

Ease the EU postdoc job market with better routes to innovation

Designing allosteric modulators to change GPCR G protein subtype selectivity

Performance evaluation of microbial fuel cell using ceramic anode blended with rice husk ash and mild steel dust

Feasibility and acceptability of using the BabySaver resuscitation platform and NeoBeat together for neonatal resuscitation in a low-resource setting: A pre-post implementation study

Background BabySaver and NeoBeat devices have the potential to enable bedside neonatal resuscitation, with an intact cord in the presence of the mother. We assessed the feasibility and acceptability of using them together for neonatal resuscitation in a low-resource setting. Methods This was a mixed methods study conducted over a period of 11 months at Mbale Hospital in Uganda. We enrolled 150 mother-infant dyads into a pre-post study. During the pre-implementation phase, neonatal resuscitation was conducted based on the existing standard of care whilst in the post-implementation phase we evaluated the BabySaver and NeoBeat. Our primary outcome was the proportion of babies resuscitated at the bedside with an intact cord. Using in-depth interviews and an inductive thematic analysis approach, we also explored experiences of health workers and mothers with use of the BabySaver and NeoBeat. Results Bedside resuscitation increased significantly in the post-implementation period (9.3% versus 45.3%, p  < 0.001 while early cord clamping decreased (26.7% versus 12.0%, p  = 0.042). The median time to successful resuscitation was shorter post-implementation (5 versus 8 minutes, p  < 0.001). Infants in the post-implementation phase had higher axillary temperatures at birth and at 0-, 10-, 20-, and 30-minutes post-resuscitation. Neonatal morbidity was lower: APGAR score <7 at 5 minutes (aPR: 0.36; 95%CI: 0.26–0.50), transfer to postnatal ward with mother (aPR: 9.27; 95%CI: 2.23–38.48), transfer to neonatal unit (aPR: 0.66; 95%CI: 0.56–0.78). Health workers found the devices easy to use, and bedside resuscitation reassured mothers, fostering trust and satisfaction. Barriers included misconceptions about delayed cord clamping, hypothermia concerns, cross-infection risks, and difficult use in theatre. Conclusion The BabySaver and NeoBeat improved bedside neonatal resuscitation and reduced morbidity. Bedside resuscitation was also acceptable to the health workers and mothers. Scaling up should address misconceptions about delayed cord clamping and optimize usability in theatre settings where many asphyxiated infants are delivered.

Will blockbuster obesity drugs revolutionize addiction treatment?

Ethically sourced image data set encourages fairness in AI research

Correction: Impact of image preprocessing methods on MRI radiomics feature variability and classification performance in Parkinson’s disease motor subtype analysis

Violent deaths following disasters: A retrospective analysis

Introduction Negative mental health outcomes associated with disaster exposure can increase risks of interpersonal and self-directed violence. However, the link between disaster exposure and increased incidence in violent deaths is not clearly established. Study objective The objective of this work is to assess the incidence of violent deaths in non-disaster and disaster periods across five distinct disaster events. Methods This study uses an ecological, quasi-experimental design to assess violent deaths in pre-disaster and disaster periods across five U.S. states (e.g., North Carolina, Oregon, Oklahoma, Wisconsin, and Colorado) with federally declared disasters resulting from natural hazards (e.g., flood, wildfire, tropical cyclone, storms). Deaths recorded in the National Violent Death Reporting System were used to describe violent deaths with injuries occurring three-months prior to and after disaster onset. Poisson regression with population offsets and fixed effects was used to calculate incidence rate ratios for disaster affected and unaffected counties within the same state, comparing violent death rates in disaster periods with non-disaster periods. Results Most of all deaths were White (80.75%), male (76.87%), and unmarried (65.03%); the median age of decedents was 41.5 years (IQR: 28–55). Overall, the incidence of violent deaths was consistent between disaster and non-disaster periods for both affected (IRR: 0.985; 95% CI: 0.760–1.276) and unaffected counties (IRR: 1.062; 95% CI: 0.975–1.158). Rate ratios were heterogenous but not significant across individual disasters, with only non-suicide and all-cause violent deaths increasing significantly following severe storms and flooding in Wisconsin for counties ineligible for public assistance Conclusion The results of this study are consistent with the heterogenous findings on violent deaths and disasters throughout literature. As disasters become more frequent and severe, it is important to further consider the relationship between disaster impacts and negative mental health outcomes, including violent deaths.

Erysipelothrix rhusiopathiae clone reemergence in association with a multi-year mass mortality event in high Arctic muskoxen (Ovibos moschatus)

The Advanced Organ Support (ADVOS) hemodialysis system fulfills its intended purpose: Analysis of data from 282 patients from the Registry on Extracorporeal Multiple Organ Support (EMOS)

Several case series have highlighted the ADVOS hemodialysis system’s efficacy in eliminating water-soluble and protein-bound substances across diverse patient populations, such as multiorgan failure, acute-on-chronic liver failure (ACLF), acidosis, and even COVID-19. The EMOS-Registry, a non-interventional, multi-center patient registry, amassed real-world evidence, culminating in the largest patient cohort treated with ADVOS to date. This study aims to present and analyze the final performance and safety outcomes from the entire dataset comprising 282 participants. Data spanning from January 18, 2017, to August 31, 2020, were collected from five German hospitals, encompassing subsets of patients with acidosis and ACLF grade 3. Performance and safety were assessed through vital signs, clinical laboratory parameters and blood gas analyses. The SOFA Score-Standardized Mortality Ratio (SMR) served to evaluate patient outcomes in the absence of a control group. Participants, with a median age of 58 years, predominantly male (64%), exhibited a high requirement for mechanical ventilation (68%) and vasopressors (82%) with a median SOFA Score of 15. Notably, a median of 3 (IQR 2, 5) ADVOS sessions per patient were administered. Following the initial treatment, significant reductions were observed in bilirubin (−1.9 [CI 95% −1.3, −2.5]), creatinine (−0.5 [−0.4, −0.6]), and blood urea nitrogen (−13.1 mg/dL [−10.3, −16.0]) levels. Moreover, there were marked improvements in blood pH (7.34 vs. 7.41, p  < 0.001), HCO 3 - (19.4 vs. 24.6 mmol/l, p  < 0.001) and base excess (−5.6 vs. 0.2 mmol/l, p  < 0.001). The observed mortality rate (66%) was notably lower than the expected rate based on SOFA Score (84%), resulting in a SMR of 0.79 (95% CI: 0.66–0.93), with a calculated number needed to treat (NNT) of 5.8. This study emphasizes the ADVOS system’s efficacy in eliminating water-soluble and protein-bound substances and correcting acid-base imbalances across a diverse cohort with multiorgan failure. However, further validation through randomized controlled trials is warranted to solidify these findings. Trial registration DRKS00017068. Registered 29 April 2019 – Retrospectively registered, https://drks.de/search/en/trial/DRKS00017068

Structures control gold mineralization in the western Allaqi shear belt of Egypt from high-precision geophysical and remote sensing datasets

Abstract This study employs Remote Sensing, advanced aeromagnetic edge detectors, and fieldwork to map structural features influencing mineralization in Egypt’s western Allaqi shear belt. Four edge detectors were tested on synthetic models; the hyperbolic tangent function and a novel edge detector were most effective at delineating edges and lineaments. These were applied to RTP aeromagnetic data to identify shallow and deep structures. The belt features an E-W striking, steeply north-dipping foliation (S1), overturned and recumbent folds (F1), and shear zones from serpentinite emplacement over volcaniclastic metasediments and metavolcanics. Thrust planes have been deformed by D2 folds with west-plunging hinges and steeply dipping cleavages oriented NE and ENE. D3 deformation turned east–west and northwest-trending folds into north-trending ones due to shearing, giving the region a N-trending fold pattern. D4 caused northeast-trending folds from shear zones; D5 formed faults in ENE-WSW, NE-SW, and N-S directions. D4 structures control gold deposits in WASB, with S4 foliation, NE-trending folds, and shearing. Haimur Au deposits align with main shearing; Um Ashira Au intersects rocks; Hariari Au trends ENE. Landsat-8 bands identified minerals like ferrous and ferric oxides, hydroxyl alterations, and chlorite zones. Higher lineament density links to increased fracturing and mineralization. Two maps highlight ore-rich areas. Combining data improves understanding of tectonic evolution and mineralization, enhancing exploration in complex terrains.

Predicting economic activity using atmospheric nitrogen dioxide (NO2) satellite data: Evidence from local economic indicators in Japan

Accurate and timely measurement of subnational economic activity is crucial for policymakers during economic crises, natural disasters and pandemics such as COVID-19. The availability of such measurement enables policymakers to identify affected regions quickly, allocate emergency resources efficiently, and target fiscal interventions. Satellite-based indicators such as nighttime lights data can be used for such purposes. Nighttime lights data are now widely used to measure economic activity, yet recent studies have highlighted several limitations, including saturation in densely populated areas, omission of daytime activity, inconsistencies among satellite sensors, and measurement errors in regions without electrification. To address these issues, this study evaluates nitrogen dioxide (NO₂) as an alternative satellite-based indicator of regional economic activity in Japan. NO₂, primarily emitted from combustion processes in transportation and industry, provides a direct measure of economic production that complements nighttime lights data. Using two-way fixed-effects panel regressions, we examine the relationship between NO₂ concentrations and prefectural gross domestic product across multiple sectors. At a spatial resolution of 0.25 degrees (0.25°), NO₂ concentrations exhibit statistically and economically significant associations with gross domestic product across most sectors, with particularly strong relationships in energy-intensive industries. However, when higher-resolution data (0.1 degrees (0.1°)) are used, most coefficients lose statistical significance, and some reverse sign in ways that contradict theoretical expectations. These results highlight both the advantages of using NO₂ over nighttime lights data for measuring subnational economic activity and the importance of appropriate spatial scale. Our findings suggest that moderate-resolution satellite data may more accurately capture regional economic patterns than finer-resolution alternatives, provided the data are properly calibrated.

Drinking water is at risk during warfare — better protections are needed

Evaluation of various traditional machine learning techniques for predicting the acute effect of different hamstring muscle stretching methods among male soccer players

Abstract This study investigated the acute effects of static (SS), dynamic (DS), and ballistic (BS) hamstring stretching on performance in male soccer players and applied machine learning (ML) to predict protocol efficacy. A total of 249 players with and without hamstring shortening completed each protocol across three sessions with 72 h of rest. Hamstring shortening classified via passive knee extension test (> 32.2° knee angle). Flexibility, strength, sprint, power, and agility were measured pre- and post-stretching. Each protocol: 4 sets × 30 s (holds/swings/bounces at 50–60 bpm), 10 s rest. ML models (k-NN, SVM, random forest) were trained on pre–post difference scores, with feature selection applied to identify key predictors and Synthetic Minority Over-sampling Technique used to address class imbalance. Findings indicate SS optimally acutely improves flexibility, whereas DS offers broader immediate performance benefits for a subsequent activity. Combining feature selection and data balancing increased k-NN accuracy to 53% (only ~ 20% points above the chance level of 33.3% for this three-class problem), highlighting methodological challenges in predicting individual responses. Exploratory analysis using ML using synthetic minority over-sampling technique reached a peak accuracy of 53.06% (compared to a baseline of 33.3%), demonstrating the promise of the approach but also highlighting the challenges of applying ML to predict individual responses to stretching interventions, underscoring the need for larger datasets and more advanced models.