Benzodiazepines are used for their anxiolytic, antiepileptic, muscle relaxant and hypnotic effects. In vitro, diazepam is predominantly metabolized to temazepam and nordiazepam (N-desmethyldiazepam). Since acetylcholinesterase is involved in the metabolism of diazepam, inhibition of the enzyme activity may have a significant effect on the therapeutic effect of the drug. To determine the inhibitory effect of 2,2,4-trimethyl-2,3-dihydro-1H-benzo[b][1,4]diazepine on acetylcholinesterase enzyme activity by conducting a comprehensive analysis that includes: measuring the enzyme activity in the presence of various concentrations of the inhibitor, determining the type of inhibition through kinetic studies, and assessing the potential therapeutic applications of the inhibitor in conditions associated with acetylcholinesterase dysfunction. In this study, the inhibitory properties of 2,2,4-trimethyl-2,3-dihydro-1-Hbenzo[ b][1,4]diazepine on the activity of the enzyme acetylcholinesterase were tested spectrophotometrically at three different temperatures of 25℃, 30℃, and 37℃. The substance was synthesized by a condensation reaction between o-phenyldiamine and acetone in the presence of phosphorus oxychloride on solid support (MgO). The solid product was obtained by crystallization from n-hexane. Each tested sample contained an appropriate concentration of the substrate acetylcholine iodide (AChI) in the range from 1.00 to 4.00 mmol·L-1; 5,5-dithiobis(2-nitrobenzoic acid) (DTNB) concentration 3 mmolL-1, phosphate buffer (KH2PO4/K2HPO4) pH value 8, tested substance concentration (17.70, 35.40, 53.10 mmol·L-1), and acetylcholinesterase solution (AChE) activity 0.54 UmL-1. Using the spectrophotometric method, it was concluded that the examined diazepine shows a competitive type of inhibition on the enzyme acetylcholinesterase. 30°C was determined to be the optimal assay temperature. The highest inhibition was observed at 25°C using 53.10 mmol·L⁻¹ of the inhibitor. As the temperature increases, the inhibition decreases. Based on the Lineweaver-Burk diagram, we gain insight into the type of inhibition exhibited by the synthesized compound. The intercept on the ordinate remains unchanged; the slope of the line increases, and the intercept on the abscissa decreases, indicating that it is a competitive inhibition. Considering the results obtained by spectrophotometric analysis, it was concluded that the enzyme acetylcholinesterase follows the Michaelis-Menten model. It has been proven that the synthesized compound exhibits inhibitory properties on the activity of acetylcholinesterase.

Open-source RISC-V CPU architectures provide FPGA developers with fine-grained control over resource utilization and performance. This work presents a case study in throughput maximization and PPA (power, performance, area) optimization for a minimal RISC-V core on FPGA, with an emphasis on structured SystemVerilog design practices. We propose a short, single-cycle pipeline architecture targeting resource-constrained deployments and systematically compare its PPA characteristics against similar performance-class implementations. FPGA-specific optimizations, including tailored Register File and ALU configurations, are employed to improve critical path timing and overall throughput. The resulting design, eduBOS5, achieves a 2× increase in DMIPS/MHz while reducing LUT utilization by 24% compared to PicoRV32 on the Gowin LittleBee FPGA. PPA metric scaling over different FPGAs was addressed by porting the design to Xilinx and Lattice devices.

Immune checkpoint inhibition (ICI) has revolutionised cancer care, but many patients do not mount anti-tumor activity and most develop autoimmune toxicity. Mechanisms and risk factors underlying ICI response and immune related adverse events (irAEs) are incompletely understood. Thus, patient stratification and targeted irAE treatments are significant unmet clinical needs. Here, we use high-throughput spectral cytometry with machine-learning based analytics to characterise longitudinal immune dynamics under ICI. 706 cryopreserved PBMC samples from 137 patients consented to the EXACT study (NCT05331066) were utilised. All patients received standard of care adjuvant or advanced ICI for skin or renal cancer. Best overall response was annotated per RECIST 1.1(Responders: CR, PR, SD > 6 months). Patients on adjuvant ICI were designated as no-relapse at > 6 months from ICI initiation. irAEs were graded per CTCAE v5 and grade ≥3 considered severe. PBMCs were stained with 3 antibody panels comprising 114 markers. Data was acquired on a Sony ID7000 spectral analyser. Systems-level characterisation of 23,906 discrete PBMC subsets per sample was performed using IMU Biosciences’ proprietary machine learning platform. Following data QC, feature selection was refined through titration, variance, and correlation filtering. Predictive PBMC signatures were derived at baseline(BL) and C2 using univariate feature selection with bootstrapping followed by stepwise logistic regression, then validated through 100 iterations of 80:20 cross validation. PBMC types associated with irAE onset(Dev), increasing severity(Inc), and resolution(Res) compared to non-irAE on treatment controls were determined (t-test in a linear mixed effects model). Using these cell types, we then repurposed the Slingshot pseudotime method to derive patient trajectories from BL to Dev, and progression to Inc and/or Res. Benefit(responder/no-relapse) prediction achieved AUCs (mean ± SD) of 0.814±0.11 (BL), and 0.85±0.10 (C2). Severe irAE prediction achieved AUCs of 0.84±0.08 (BL), and 0.82±0.13 (C2). Dev and Inc samples of severe irAEs showed significant enrichment of activated non-classical monocytes, CD4 T, CD8 T, gd T, and NK cells. Dev of non-severe irAEs was indistinguishable from controls. In pseudotime, we found a bifurcating trajectory from BL to severe Dev vs. non-severe Dev. A further bifurcating trajectory distinguished progression from BL to on-treatment, then Dev vs. BL to Dev, then Inc. Res represented a return towards on-treatment controls in both lineages. Here, we used high-content PBMC profiling to generate immune signatures predictive of ICI outcome with compelling accuracy. We additionally gain mechanistic insights into irAE development and progression to severity. Our findings highlight the transformative potential of machine learning-powered immune profiling to identify predictors and drivers of benefit and toxicity outcomes under ICI with clear implications for patient stratification and irAE management. Max Emmerich, Duncan McKenzie, Carla Castignani, Jack Bibby, Jennie Yang, Marija Miletic, Laura Marandino, Zayd Tippu, Jonathan Lim, Taja Barber, Stephanie Hepworth, Paul Rouse, Lilian Williams, Kim Edmonds, Justine Korteweg, Serena Vanzan, James Larkin, Tom Hayday, Adam Laing, Samra Turajlic. Comprehensive blood profiling for immunotherapy outcome prediction and longitudinal immune trajectory characterisation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A002.

Obesity, a global health concern defined by excessive adiposity and persistent metabolic imbalance, has far-reaching implications that extend beyond standard metabolic and cardiovascular comorbidities. While the association between obesity and reproductive dysfunction is well-established, the precise molecular mechanisms underlying these associations remain incompletely understood, particularly as regards the distinction between obesity-specific effects and those mediated by dietary components or metabolic syndrome. The present review integrates currently available knowledge on the mechanisms through which obesity impairs reproductive function in both sexes, from gametogenesis to postnatal development. In males, obesity drives testicular inflammation, disrupts spermatogenesis, impairs sperm motility and DNA integrity, and alters key signaling pathways, with oxidative stress and metabolic endotoxemia as central mediators. In females, obesity induces ovarian dysfunction, alters steroidogenesis, compromises oocyte quality and disrupts follicular environments, leading to reduced fertility and adverse pregnancy outcomes. However, the relative contribution of obesity-induced inflammation vs. direct lipotoxic effects remains poorly characterized in both sexes. The present review further examines the impact of parental obesity on fertilization capacity, placental function and in utero development, highlighting sex-specific and intergenerational effects mediated by mitochondrial dysfunction and epigenetic modifications. Notably, maternal obesity impairs placental and fetal organ development, increases the risk of metabolic and reproductive disorders in offspring, and alters key developmental signaling pathways. While some studies suggest that lifestyle interventions and antioxidant therapies may partially reverse obesity-induced reproductive impairments, significant gaps remain in understanding the precise molecular mechanisms and potential for therapeutic rescue. By synthesizing findings from animal models and human studies, the present review highlights the pivotal role of oxidative stress as a mechanistic link between obesity and reproductive dysfunction. It emphasizes the need for further research to inform clinical strategies aimed at mitigating these adverse outcomes.

AIMS The Central/Eastern Europe (CEE) Quality of Care Centres (QCC) Survey evaluated the implementation of guideline-directed medical therapies (GDMT) and device use at discharge after heart failure (HF) hospitalization in CEE, where GDMT underutilization remains a concern. METHODS AND RESULTS Between March 2024 and January 2025, 2251 patients (mean age 70.0 years, 60.4% male) were enrolled at discharge from 21 centres across 12 CEE countries. The patient population included HF with reduced ejection fraction (HFrEF) (55.5%), HF with mildly reduced ejection fraction (15.3%) and HF with preserved ejection fraction (27.9%). In the total population, from admission to discharge there was a increase in the use of angiotensin receptor-neprilysin inhibitor (ARNI) (17.1% to 34.3%), beta-blockers (69.4% to 92.4%), mineralocorticoid receptor antagonists (MRA) (44.0% to 82.1%) and sodium-glucose co-transporter 2 inhibitors (SGLT2i) (30.8% to 79.9%), with a reduction in angiotensin-converting enzyme inhibitor (ACEI) use (all p < 0.05). Similar trends were observed across HF phenotypes, including HFrEF (increased use of ARNI, 26.3% to 55.1%, beta-blockers, 69.8% to 95.3%, MRA 49.5% to 89.0%, and SGLT2I 36.2% to 79.8%, and lower ACEI use, all p < 0.05). At discharge, 53.5% of patients received quadruple therapy (63.9% with HFrEF), while ≥50% target doses of titratable drugs were achieved in 18.8% (17.8% in HFrEF). Predictors of GDMT underuse included older age, lower education, living alone, non-ischaemic HF, higher ejection fraction, chronic kidney disease, hypotension, hyperkalaemia, prolonged hospitalization, and residual oedema. Among eligible HFrEF patients, 21.3% were discharged with, or referred for implantable cardioverter-defibrillator, and 17.4% for cardiac resynchronization therapy. CONCLUSIONS The CEE-QCC Survey highlights substantial in-hospital GDMT implementation and up-titration, though device use remains limited. Targeted strategies are needed to enhance guideline implementation and ensure optimal HF care across the CEE region.

Background The increasing global prevalence of mental disorders as well as a persistent stigma make mental disorders a public health priority. The aim of this study was to provide a comprehensive overview of psychotropic drugs utilization from 2006 to 2021 in the Republic of Serbia, examining both pre pandemic and pandemic-related changes. Methods To conduct this descriptive study, publicly available data on psychotropic drugs were retrieved from the official website of the Agency for Medicines and Medical Devices of Serbia (ALIMS). The linear and joinpoint regression were used in data analysis. Results A total of 54 psychotropic drugs use was analyzed from 2006 to 2021. There was an increase in the consumption of antidepressants, atypical antipsychotics, anxiolytics, sedatives, hypnotics, anti-dementia drugs and gabapentinoid-based drugs. The increase in the consumption of the psychotropic drugs was linear, with no differences between the pre-COVID-19 period and the COVID-19 pandemic. Contrary, a significant decrease in use was observed for some antidepressants (maprotiline, moclobemide, mianserin), antipsychotics (chlorpromazine, fluphenazine), psychostimulants and nootropic drugs (piracetam), anxiolytics (diazepam, prazepam), sedatives and hypnotics (midazolam). Conclusion The COVID-19 pandemic did not contribute to change in consumption of psychotropic drugs in Serbia. Still, the use of antidepressants, atypical antipsychotics, anxiolytics, sedatives, hypnotics, anti-dementia drugs and gabapentinoids increased from 2006 to 2021.

The widespread deployment of autonomous systems in safety-critical environments such as urban air mobility hinges on ensuring reliable, performant, and safe operation under varying environmental conditions. One such approach, value function-based safety filters, minimally modifies a nominal controller to ensure safety. Recent advances leverage offline learned value functions to scale these safety filters to high-dimensional systems. However, these methods assume detailed priors on all possible sources of model mismatch, in the form of disturbances in the environment -- information that is rarely available in real world settings. Even in well-mapped environments like urban canyons or industrial sites, drones encounter complex, spatially-varying disturbances arising from payload-drone interaction, turbulent airflow, and other environmental factors. We introduce SPACE2TIME, which enables safe and adaptive deployment of offline-learned safety filters under unknown, spatially-varying disturbances. The key idea is to reparameterize spatial variations in disturbance as temporal variations, enabling the use of precomputed value functions during online operation. We validate SPACE2TIME on a quadcopter through extensive simulations and hardware experiments, demonstrating significant improvement over baselines.

For decades the strong-field approximation (SFA) has been a theoretical backbone for describing the strong-field related phenomena such as above-threshold ionization (ATI) and high-order harmonic generation, even though it is well-known that it cannot accurately account for the long-range Coulomb interaction between the liberated electron and residual atomic ion. In this paper, we theoretically investigate high-order ATI. We use numerical solutions of the time-dependent Schrödinger equation (TDSE) and an improved SFA that includes electron rescattering. The analysis is performed for atomic anions and neutral atoms exposed to elliptically polarized laser fields. To validate the SFA and test its applicability, we compare both theoretical approaches for various targets and laser field parameters. We also show that the improved SFA in which the final electron plane wave is replaced by the Coulomb distorted plane wave leads to a better agreement with the results obtained using the solutions of the TDSE.

PURPOSE Accurate target volume delineation is critical for effective stereotactic radiotherapy (SRT) of brain metastases. This study systematically investigates how MRI sequence selection and the time elapsed after contrast agent (CA) administration affect the apparent metastases volumes, with the goal of optimizing MRI protocols for radiation therapy planning. MATERIALS AND METHODS A total of 49 patients with 414 brain metastases were included and randomized into 6 groups with varying imaging sequences (MPRAGE, SPACE, and VIBE) and timepoints after CA administration. Lesions smaller than 0.03 cm3 were excluded due to resolution limitations. Lesion volumes were independently assessed by radiology and radiation oncology specialists, and mean values were analyzed. The effects of MRI sequence and time delay on lesion volume were evaluated using t tests, ANOVA, and multiple linear regression. RESULTS Both MRI sequence and CA timing significantly influenced measured volumes. On average, SPACE volumes were 20% larger than MPRAGE, and VIBE volumes were 10% larger than SPACE, independent of timing. Lesion volumes increased progressively with time after CA administration at rates of 0.63%, 0.58%, and 0.36% per minute for MPRAGE, SPACE, and VIBE, respectively. Smaller lesions (<1 cm3) showed greater relative intersequence differences, primarily due to variations in visible lesion borders. CONCLUSIONS Both MRI sequence choice and imaging time after CA administration significantly affect the apparent volume of brain metastases in SRT planning. Although SPACE and VIBE sequences enhance small lesion detection, they may also increase border blurring and inter-rater variability. Standardizing protocols to account for these factors is essential for improving delineation accuracy, reducing toxicity risk, and optimizing SRT outcomes.

BACKGROUND White cord syndrome (WCS) is a rare and extremely serious complication that can occur following spinal decompression procedures for severe mostly cervical spinal stenosis. It is often reported immediately after surgery or several hours to days postoperatively and is identified via a diagnosis of exclusion based on new-onset sudden motor weakness after a decompression procedure. OBSERVATIONS The authors report the illustrative case of a 54-year-old female patient with WCS, who was managed with surgical intervention, corticosteroid therapy, and mean arterial blood pressure support. Additionally, the authors systematically reviewed an additional 27 cases of WCS documented in the literature. LESSONS A relatively favorable clinical outcome was observed in this patient following surgical intervention combined with corticosteroid therapy and mean blood pressure support. Currently, there are no established guidelines for the treatment of WCS; however, in any patient experiencing sudden neurological deterioration after cervical spinal decompressive surgery—especially when a known cause is unidentified—WCS should be considered as a potential diagnosis, and prompt treatment should be initiated to attempt to improve outcomes. https://thejns.org/doi/10.3171/CASE25542

This study examines the role of public procurement in stimulating innovation in the United States, with particular attention to small and medium‐sized enterprises (SMEs). Public procurement of innovation (PPI) is widely regarded as a demand‐side policy instrument that can generate lead markets for emerging technologies, yet its scope and structural dynamics remain underexplored in large economies. To address this gap, we analyze more than 46 million procurement contracts (2007–2021) from USAspending.gov using a text‐mining approach informed by disruptive technology keywords from Bloom et al. We find that innovation‐related contracts constitute only 0.12% of the total, with SMEs securing around 41%. Nonetheless, SMEs face persistent difficulties in winning follow‐on contracts, reflecting resource limitations. Moreover, results reveal an inverted U‐shaped relationship between competition and innovation procurement, indicating that moderate competition fosters innovation most effectively. The study contributes to theory by extending the Resource‐Based View (RBV) to public procurement, demonstrating how resource heterogeneity and immobility shape outcomes in government‐led markets. Practically, we provide policy insights to strengthen SME participation in PPI through targeted support and simplified procedures, thereby advancing both innovation policy and SME competitiveness.

BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy, despite the growing recognition of the importance of bone health in individuals with epilepsy. Associations one statistical method for finding correlations between variables in big datasets is called association rule mining (ARM). This technique finds patterns of common items or events in the data set, including associations. Through the analysis of patient data, including demographics, genetic information, and reactions with previous treatments, ARM can identify harmful drug reactions, possible novel combinations of medicines, and trends which connect particular individual features to treatment outcomes. AIM To investigate the evidence on the effects of anti-epileptic drugs (AEDs) on calcium metabolism and supplementing with vitamin D to help lower the likelihood of bone-related issues using ARM technique. METHODS ARM technique was used to analyze patients’ behavior on calcium metabolism, vitamin D and anti-epileptic medicines. Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study. There were three patient groups: Group 1 received one AED, group 2 received two AEDs, and group 3 received more than two AEDs. The researchers analyzed the alkaline phosphatase, ionized calcium, total calcium, phosphorus, vitamin D levels, or parathyroid hormone values. RESULTS A total of 150 patients, aged 12 years to 60 years, were studied, with 50 in each group (1, 2, and 3). 60% were men, this gender imbalance may affect the study’s findings, as women have different bone metabolism dynamics influenced by hormonal variations, including menopause. The results may not fully capture the distinct effects of AEDs on female patients. A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs. 86 patients had generalized epilepsy, 64 partial. 42% of patients had AEDs for > 5 years. Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy. Polytherapy elevated alkaline phosphatase and phosphorus levels. CONCLUSION ARM revealed the possible effects of variables like age, gender, and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication.

Decoding how specific neuronal subtypes contribute to brain function requires linking extracellular electrophysiological features to underlying molecular identities, yet reliable in vivo electrophysiological signal classification remains a major challenge for neuroscience and clinical brain-computer interfaces (BCI). Here, we show that pretrained, general-purpose vision-language models (VLMs) can be repurposed as few-shot learners to classify neuronal cell types directly from electrophysiological features, without task-specific fine-tuning. Validated against optogenetically tagged datasets, this approach enables robust and generalizable subtype inference with minimal supervision. Building on this capability, we developed the BCI AI Agent (BCI-Agent), an autonomous AI framework that integrates vision-based cell-type inference, stable neuron tracking, and automated molecular atlas validation with real-time literature synthesis. BCI-Agent addresses three critical challenges for in vivo electrophysiology: (1) accurate, training-free cell-type classification; (2) automated cross-validation of predictions using molecular atlas references and peer-reviewed literature; and (3) embedding molecular identities within stable, low-dimensional neural manifolds for dynamic decoding. In rodent motor-learning tasks, BCI-Agent revealed stable, cell-type-specific neural trajectories across time that uncover previously inaccessible dimensions of neural computation. Additionally, when applied to human Neuropixels recordings–where direct ground-truth labeling is inherently unavailable–BCI-Agent inferred neuronal subtypes and validated them through integration with human single-cell atlases and literature. By enabling scalable, cell-type-specific inference of in vivo electrophysiology, BCI-Agent provides a new approach for dissecting the contributions of distinct neuronal populations to brain function and dysfunction.

Based on an early 2020s survey of 3,500 police officers from nine countries, this paper explores perceived community and police adherence to the COVID-19 regulations. We propose that both public and police perceived adherence with the COVID-19 rules are related not only to individual-level factors (e.g., gender, concern for own health) but also to country-level factors (e.g., quality of governance, protection of citizens' rights). Our findings reveal that individual-level factors, such as the concerns for personal and family health, were strong and consistent predictors of perceived community and police adherence. While misinformation about the COVID-19 pandemic was associated with the perceived community adherence, it was not related to the perceived police adherence. Moreover, country-level factors, such as the stringency of COVID-19 rules and regulations and the protection of citizens' rights, were also significantly related to both perceived community and police adherence.