Browse Articles

Feasibility, user satisfaction, and knowledge improvement after a VR training program for healthcare professionals managing behavioral and psychological symptoms of dementia (BPSD): Protocol for the FORMSPC-REALVI single-arm pre-post study

Background Behavioral and psychological symptoms are a common challenge for healthcare professionals when managing patients with dementia, and effective verbal and nonverbal communication skills are crucial in caring for such patients. Objectives This article describes a research study protocol for investigating the effectiveness of a virtual reality (VR) training program for healthcare professionals in managing disruptive behavioral and psychological symptoms of dementia (BPSD), such as aggressiveness, agitation, and care refusal. Methods The training scenarios were co-designed with ten healthcare professionals and implemented using an immersive 3D VR platform. Forty geriatric healthcare professionals will participate in a 2-hour training session using VR movies and a Moodle-based theoretical reinforcement. Before and after the training, participants will complete self-assessment questionnaires and knowledge-based quizzes designed to evaluate their perceived competence and understanding of appropriate communication strategies with patients displaying BPSD. The primary outcome will be the change in quiz scores between the pre- and post-training evaluations. Secondary outcomes include training satisfaction, perceived competence, and system usability. Hypotheses Trial registration This study is registered in the French General Data Protection Regulation (GDPR) registry of Assistance Publique – Hôpitaux de Paris (N° 2022 0518135339–18 May 2022). As the trial targets health-providers and measures effects only on them (and not on providers’ patients), clinical trial registration is not required (see ICMJE guidelines: https://www.icmje.org/about-icmje/faqs/clinical-trials-registration/).

Highly Selective Oxygen Electroreduction to Hydrogen Peroxide on Sulfur‐Doped Mesoporous Carbon

Abstract As a paradigm‐shifting material platform in energy catalysis, precisely engineered ordered mesoporous carbon spheres emerge as supreme metal‐free electrocatalysts, outperforming conventional carbon‐based counterparts through synergistic structural and electronic innovations. Herein, we architecturally design vertically aligned cylindrical mesoporous carbon spheres with atomic‐level sulfur doping (S‐mC) that establish unprecedented performance benchmarks in the two‐electron oxygen reduction reaction (2e − ‐ORR) to hydrogen peroxide. Systematic comparative studies reveal that the S‐mC catalysts achieve exceptional H 2 O 2 selectivity (>99%) and activity at current density of −3.5 mA cm −2 , surpassing state‐of‐the‐art metal‐free catalysts in current density. Impressively, the optimized S‐mC electrocatalyst in a flow cell device achieves an exceptional H 2 O 2 yield of 25 mol g catalyst −1  h −1 . The carbon matrix's unique sp 2 /sp 3 hybrid network coupled with S‐induced charge redistribution generates electron‐deficient hotspots that selectively stabilize *OOH intermediates, as evidenced by in situ spectroscopic characterization and DFT calculations. This structural–electronic synergy endows the carbon framework with metal‐like catalytic efficiency while maintaining inherent advantages of chemical robustness and cost‐effectiveness. The marriage of S‐doping engineering with mesoscopic pore architecture control opens a new way for developing efficient carbon‐based electrocatalysts for oxygen selective reduction to H 2 O 2 .

Identifying the effectiveness of face mask in a large population with a network-based fluid model

Face masks are important in respiratory disease control, yet their effectiveness varies widely depending on the mask material and its fit on the wearer’s face. In this study, a new semi-analytical flow network model based on the Kármán-Pohlhausen technique is introduced and utilized to efficiently assess mask performance across diverse facial features that represent the observed variations inside a large population. The reduced-order model enables the evaluation of the role of different facial geometrical features with significantly lower computational costs compared to traditional computational fluid dynamics simulations. This research reveals that the area around the nose, particularly without a nose clip, is most susceptible to peripheral leakage and high-velocity jets due to larger gaps. It is argued that subtle variations in facial features, especially the zygomatic arch, significantly influence leakage patterns, emphasizing the importance of customized mask designs. The study also elucidates the complex role of nose clips in improving sealing efficacy for tightly fitted masks and redirecting leaked flow in typical imperfect facemasks. This dual function of nose clips significantly influences overall mask performance, though the exact impact varies depending on individual facial features and mask fit. The reduced-order fluid model presented here has the potential to quantify the effectiveness of face masks for a large population and influence the design of future face masks, with a focus on minimizing or redirecting leakage jets to mitigate the dispersion of respiratory aerosols thus enhancing public health strategies for respiratory disease control.

CausNet-partial: ‘Partial Generational Orderings’ based search for optimal sparse Bayesian networks via dynamic programming with parent set constraints

In our recent work, we developed a novel dynamic programming algorithm to find optimal Bayesian networks with parent set constraints. This ‘generational orderings’ based dynamic programming algorithm—CausNet—efficiently searches the space of possible Bayesian networks. The method is designed for continuous as well as discrete data, and continuous, discrete and survival outcomes. In the present work, we develop a variant of CausNet—CausNet-partial—where we introduce the space of ‘partial generational orderings’, which is a novel way to search for small and sparse optimal Bayesian networks from large dimensional data. We test this method both on simulated and real data. In simulations, CausNet-partial shows superior performance when compared with three state-of-the-art algorithms. We apply it also to a benchmark discrete Bayesian network ALARM, a Bayesian network designed to provide an alarm message system for patient monitoring. We first apply the original CausNet and then CausNet-partial, varying the partial order from 5 to 2. CausNet-partial discovers small sparse networks with drastically reduced runtime as expected from theory. To further demonstrate the efficacy of CausNet-partial, we apply it to an Ovarian Cancer gene expression dataset with 513 genes and a survival outcome. Our algorithm is able to find optimal Bayesian networks with different number of nodes as we vary the partial order. On a personal computer with a 2.3 GHz Intel Core i9 processor with 16 GB RAM, each processing takes less than five minutes. Our ‘partial generational orderings’ based method CausNet-partial is an efficient and scalable method for finding optimal sparse and small Bayesian networks from high dimensional data.

Complexity myths and the misappropriation of evolutionary theory

Recent papers by physicists, chemists, and geologists lay claim to the discovery of new principles of evolution that have somehow eluded over a century of work by evolutionary biologists, going so far as to elevate their ideas to the same stature as the fundamental laws of physics. These claims have been made in the apparent absence of any awareness of the theoretical framework of evolutionary biology that has existed for decades. The numerical indices being promoted suffer from numerous conceptual and quantitative problems, to the point of being devoid of meaning, with the authors even failing to recognize the distinction between mutation and selection. Moreover, the promulgators of these new laws base their arguments on the idea that natural selection is in relentless pursuit of increasing organismal complexity, despite the absence of any evidence in support of this and plenty pointing in the opposite direction. Evolutionary biology embraces interdisciplinary thinking, but there is no fundamental reason why the field of evolution should be subject to levels of unsubstantiated speculation that would be unacceptable in any other area of science.

Outside Back Cover: Data–Knowledge‐Dual‐Driven Electrolyte Design for Fast‐Charging Lithium Ion Batteries (Angew. Chem. Int. Ed. 24/2025)

Functionalized Cyclic Poly(α‐Hydroxy Acids) via Controlled Ring‐Opening Polymerization of O‐Carboxyanhydrides

Abstract Linear poly(α‐hydroxy acids) are important degradable polymers, and they can be efficiently prepared by ring‐opening polymerization of O‐carboxyanhydrides with pendant functional groups. However, attempts to prepare cyclic poly(α‐hydroxy acids) have been plagued by side reactions, including epimerization and uncontrolled intramolecular chain transfers or termination, that prevent the synthesis of high‐molecular‐weight stereoregular cyclic polyesters. Herein, we report a scalable method for the synthesis of high‐molecular‐weight (>100 kDa) stereoregular functionalized cyclic poly(α‐hydroxy acids) by means of controlled polymerization of O‐carboxyanhydrides using a catalytic system consisting of a lanthanum complex with a sterically bulky ligand and a manganese silylamide. Additionally, using this system, we could readily prepare cyclic block poly(α‐hydroxy acids) by means of sequential addition of O‐carboxyanhydrides. The obtained cyclic polyesters and their cyclic block copolyesters exhibit distinctive physicochemical properties—including elevated phase transition temperature, improved toughness, and reduced viscosity—compared to their linear counterparts.

Pain expectations, experiences and coping strategies used by post-operative patients: A descriptive phenomenological study

Objectives Post-operative pain(POP) is still an unresolved problem worldwide, including in limited-resource countries such as Ghana. Earlier studies have mainly focused on postoperative pain experiences of patient with little attention to their pain expectations and coping strategies. The current study sought to qualitatively explore pain expectations, pain experiences, and coping strategies used by adult surgical patients to help add patients’ perspectives to surgical pain management. Methods A descriptive phenomenological design approach was used to study nine purposively sampled surgical patients receiving care at a regional hospital in Ghana. Participants were individually interviewed before and during the postoperative period to share their opinions on their pain expectations, postoperative pain experiences, and coping strategies. Recruitment and data collection took place between July 8, 2021, and August 30, 2021. The semi-structured individual interviews were audio-recorded, transcribed verbatim, and content analysed to generate themes that described participants’ accounts. Results The participants consisted of six females and three males, aged 24–40, who had undergone major surgeries. This study derived three main themes: diverse pain expectations and experiences, post-operative pain effects, and post-operative pain coping strategies. The study revealed that participants had different pain expectations and experiences, and surgical pain affected their activities of daily living and emotions. Participants coped with the postoperative pain by using personal strategies and seeking support from nurses. Conclusion Pain expectation of surgical patients affects their post-operative pain experiences. Surgical patients use coping strategies in their post-operative pain management. More needs to be done in reducing surgical patients’ experience of post-operative pain.

Nickel‐Catalyzed Umpolung Difluoroalkylation of Imines Enables General Access to β‐Difluoroalkylated Amines

Abstract Fluoroalkylated amines play a pivotal role in medicinal chemistry, yet the general and efficient synthesis of β‐difluoroalkylated amines remains elusive. Here, we developed a nickel‐catalyzed umpolung strategy that enables the difluoroalkylation of 2‐azaallyl anions generated from aliphatic and aromatic imines, effectively overcoming the previous limitations. By inverting the polarity of imines, this strategy allows for the coupling of a variety of readily accessible difluoroalkyl bromides and iodides. This approach is characterized by its high efficiency, broad substrate scope, high functional group tolerance, and ease of synthesis. The rapid modification of bioactive molecules by the efficient synthesis of difluorinated analogs of key amine moieties present in bioactive molecules, including amphetamine, using the current approach shows the promising potential of this protocol in advancing drug discovery and development.

Optimizing the Selectivity of CH ₄ Electrosynthesis from CO ₂ Over Cuprates Through Cu─O Bond Length Descriptor

Abstract Precisely controlling the nature of Cu─O bond in Cu‐based oxide catalysts and understanding its correlation with CH 4 electrosynthesis (from CO 2 ) for selectivity optimization is a long‐standing challenge. Herein, taking a specific type of cuprates structured with CuO 4 square‐planar motifs as the platform, we report a selectivity descriptor of Cu─O bond length for screening highly selective catalysts toward CH 4 electrosynthesis. We establish the descriptor by systematic investigations of several proof‐of‐concept cuprates. Their Cu─O bond lengths are precisely controlled ranging from 1.944 to 1.970 Å and these bonds remain stable in CH 4 selectivity evaluation. Our investigations demonstrate that the CH 4 selectivity exhibits a volcano‐type dependence on the Cu─O bond length, and the optimized value is accessible at about 1.951 Å. This could be attributed to the optimal (neither too strong nor too weak) *CO adsorption created by the moderate Cu─O bond length, facilitating *CO hydrogenation. Furthermore, utilizing this descriptor, we predict three highly selective cuprates for CH 4 electrosynthesis, with superior selectivity that is near the top of the volcano plot. And importantly, in an acidic electrolyte (pH = 1), they outperform the reported catalysts, achieving CH 4 selectivity of up to 61.7% at 300 mA cm −2 .

A plaque recognition algorithm for coronary OCT images by Dense Atrous Convolution and attention mechanism

Currently, plaque segmentation in Optical Coherence Tomography (OCT) images of coronary arteries is primarily carried out manually by physicians, and the accuracy of existing automatic segmentation techniques needs further improvement. To furnish efficient and precise decision support, automated detection of plaques in coronary OCT images holds paramount importance. For addressing these challenges, we propose a novel deep learning algorithm featuring Dense Atrous Convolution (DAC) and attention mechanism to realize high-precision segmentation and classification of Coronary artery plaques. Then, a relatively well-established dataset covering 760 original images, expanded to 8,000 using data enhancement. This dataset serves as a significant resource for future research endeavors. The experimental results demonstrate that the dice coefficients of calcified, fibrous, and lipid plaques are 0.913, 0.900, and 0.879, respectively, surpassing those generated by five other conventional medical image segmentation networks. These outcomes strongly attest to the effectiveness and superiority of our proposed algorithm in the task of automatic coronary artery plaque segmentation.

Strategic planning as a catalyst for sustainability: A mediated model of strategic intent and formulation in manufacturing SMEs

This study examines the influence of Systematic Strategic Planning (SSP) on the Sustainable Performance (SP) of manufacturing Small and Medium Enterprises (SMEs) in Pakistan. Despite SMEs’ vital contribution to economic growth, there is limited empirical research on how strategic planning enhances sustainable performance in SMEs operating in emerging economies facing political and economic instability. Drawing on the Triple Bottom Line (TBL) and Resource-Based View (RBV) theories, this study investigates the mediating roles of Strategic Intent (SI) and Strategic Formulation (SF) in the SSP-SP relationship. A quantitative research design was employed, and data were collected through structured questionnaires distributed to senior executives and decision-makers of manufacturing SMEs. A total of 410 valid responses were received. Structural Equation Modeling (SEM) was applied using AMOS 28 software to analyze the data and test the hypothesized relationships. The results demonstrate that SSP has a significant direct effect on SP and an indirect effect through SI and SF. Specifically, the components of SSP—strategic analysis, strategy creation, strategy execution, and monitoring and evaluation—enhance SMEs’ economic, environmental, and social performance. The study highlights that adopting systematic strategic planning practices enables SMEs to navigate complex and uncertain environments, achieve competitive advantage, and contribute to sustainable development goals. This research fills a critical gap in the literature by focusing on manufacturing SMEs in Pakistan, an under-researched context in the sustainability and strategic management fields. It offers practical insights for SME managers and policymakers to develop and implement comprehensive strategic planning frameworks that foster sustainability. The study also provides theoretical contributions by integrating SI and SF as key mediators within the TBL and RBV theoretical frameworks.

A 3D Hybrid Perovskite Ferroelastic with Triclinic‐to‐Cubic Phase Transition Boosts Temperature/Pressure Dual On/Off Switchable Birefringence

Abstract Birefringent crystals have gained enormous attention for decades due to their unique ability to manipulate polarized light. However, achieving the fascinating on/off (active/inactive) switchable birefringence under external stimuli in crystals remains a huge challenge, and the stimuli employed have been constrained predominantly to temperature. Here, through H/F substitution, we designed a 3D hybrid double perovskite ferroelastic [C 3 H 5 FNH 2 ] 2 [(NH 4 )Fe(CN) 6 ] ( 1‐F ), which undergoes a ferroelastic transition at 296 K, with the highest possible orientation states of 24 among ferroelastic crystals. Notably, the ferroelastic phase transition of 1‐F can also be triggered by pressure with a low critical pressure of ∼0.3 GPa. More importantly, 1‐F shows the unprecedented temperature/pressure dual stimuli‐induced on/off switching of birefringence between the birefringence‐active state in the anisotropic triclinic ferroelastic phase and the birefringence‐inactive state in the isotropic cubic paraelastic phase during the ferroelastic transition. To the best of our knowledge, this is the first report of dual stimuli‐induced switchable birefringence in crystals. Our work paves a new way for the switching of birefringence and sheds light on the further exploration of switchable birefringence in ferroelastics.

First report of field-evolved resistance to insecticides in Spodoptera frugiperda (Lepidoptera: Noctuidae) from Punjab, Pakistan

The fall armyworm, Spodoptera frugiperda, is one of the major destructive pests of agriculture in Pakistan. The widespread use of insecticides for the management of S. frugiperda has resulted in the field-evolved resistance to insecticides in different strains worldwide. However, field-evolved resistance to insecticides has not yet been reported in S. frugiperda from Pakistan. Following reports of control failure of S. frugiperda in Punjab, Pakistan, a study was planned to investigate resistance to insecticides from different classes in field strains of S. frugiperda to confirm whether the resistance was indeed evolving. Here, we explored resistance to spinetoram, emamectin benzoate, indoxacarb, diflubenzuron, methoxyfenozide, chlorpyrifos and cypermethrin in seven field strains and compared them with a laboratory susceptible reference (Lab-SF) strain of S. frugiperda. Compared with the Lab-SF strain at the LC50 levels, the field strains exhibited 24.8–142.7 (spinetoram), 33.4–91.4 (emamectin benzoate), 30.1–90.6 (indoxacarb), 16.1–38.4 (diflubenzuron), 18.4–51.8 (methoxyfenozide), 37.1–222.9 (chlorpyrifos), and 61.9–540.6 (cypermethrin) fold resistance ratios (RRs). In the presence of detoxification enzyme inhibitors [piperonyl butoxide (PBO) and S,S,S-tributyl phosphorotrithioate (DEF)], the toxicity of all the insecticides, with the exception of spinetoram, was significantly enhanced in the tested field strains of S. frugiperda, providing insight into the metabolic mechanism of resistance. Additionally, compared with the Lab-SF strain, the resistant field strains exhibited elevated activities of detoxification enzymes such as glutathione S-transferases (GST), carboxylesterases (CarE) and mixed-function oxidases (MFO). Overall, the findings of the present study provide robust evidence of field-evolved resistance to insecticides in S. frugiperda, which needs to be managed to minimize yield losses of different crops caused by this global pest.

Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features

Cardiovascular diseases (CVDs) have surpassed cancer and become the major cause of death worldwide. An electrocardiogram (ECG) is a non-invasive and quicker method for diagnosing abnormal heart conditions. While research has extensively focused on ECG analysis for disease classification, it has been primarily directed toward binary classification or classification of Arrhythmias, highlighting the dire need for detailed classification models. This study utilises the extensive PTB-XL database ECG records to develop a robust method for classifying various heart abnormalities. The data with unique labels is filtered through the Butterworth bandpass filter and Discrete Wavelet Transform (DWT) db-8. The R-peaks of the clean signal were used to detect the subsequent morphological features, i.e., P-QRS-T intervals and amplitudes. The feature set was balanced using the Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC) and fed into Convolutional Neural Network (CNN) and Deep Neural Network (DNN) with 5-fold cross-validation. The models classified the ECG records into one normal and four abnormal classes: Conduction Disturbance (CD), Myocardial Infarction (MI), Hypertrophy (HYP), and ST-T Changes (STTC). Performance metrics such as F1 score, recall, precision, and accuracy were evaluated for each model. The CNN model achieved a mean accuracy of 81% ± 0.03, while the DNN model achieved a mean accuracy of 84% ± 0.01. One key finding is that Hypertrophy (HYP) was consistently classified with up to 98% accuracy. Thus, the study demonstrates the effectiveness of combining advanced signal processing and deep learning techniques for precise multi-class heart disease classification using P-QRS-T features, paving the way for future real-time clinical applications.

Characterization of lightning-induced overvoltages in wind farms

Wind farms are exposed to various weather hazards, including lightning strikes, which can pose significant risks. However, the impact of different wind farm topologies on the magnitude of lightning-induced overvoltages has not been extensively studied, creating a gap in existing literature. This paper addresses this gap by analyzing the characteristics of lightning-induced overvoltages injected into the grid for various wind farm topologies. The scientific scope of this study is to evaluate the influence of wind farm topology on the severity of different types of lightning-induced overvoltages including positive, negative, and double-peaked lightning strikes, using simulation-based analysis. The topologies tested include radial, single-sided ring (SSR), double-sided ring (DSR), and star topologies. The results demonstrate that radial topology leads to the highest overvoltage injection, while switching to SSR, DSR, or star topologies results in reductions of overvoltage by 11.5% to 51.0%, 39.5% to 66.0%, and 62.3% to 89.0%, respectively. These results support a topology-based risk assessment approach, offering clear guidance for selecting configurations that improve lightning resilience.

Markov approach for inventory control with meta-heuristics in intermittent demand environment

Demand variability directly affects inventory management. The variability of intermittent demand causes high lost sales or holding costs. While lost sales reduce customer satisfaction, keeping excessive stock also creates high costs for companies. This situation can be prevented with an appropriate inventory policy. In this study, a Markov-based proactive inventory management approach supported by metaheuristic methods is proposed in the inventory management of intermittent demands. The main contribution of the proposed approach is to find a lower and upper limit for stock by modeling the intermittent demands in the past period with the Markov process. With these optimized limits, it is aimed to balance the largest costs caused by intermittent demands, namely stock and lost sales costs. The intermittent demands used were randomly generated in 4 different sizes from small to large. The proposed approach contributes to inventory management by minimizing the negativities caused by demand variability through the Markov process. A mathematical model has been proposed for stock level optimization, but no feasible solution has been found. The mathematical model was transformed into a fitness function and a solution was provided with the Tabu Search Algorithm and Simulated Annealing. The inventory management process of intermittent demand was first evaluated without the Markov approach, and then the Markov approach was included in the process. The results showed that the Markov approach was a good tool for inventory management of intermittent demand. When the results were examined, the stock limits computed with the Markov process balanced the increased inventory cost and lost sales costs due to intermittent demand.

Structural Basis of Sequential Enantioselective Epoxidation by a Flavin‐Dependent Monooxygenase in Lasalocid A Biosynthesis

Abstract Polyether polyketides are a structurally diverse group of natural products known for their antimicrobial and antiproliferative activities. Lasalocid A is a canonical natural polyether produced by the soil bacterium Streptomyces lasalocidi . In lasalocid A biosynthesis, a polyene polyketide intermediate is converted into a bisepoxide by the flavin‐dependent monooxygenase enzyme Lsd18. Remarkably, Lsd18 acts on two distinct C═C groups in the substrate molecule, forming two ( R , R ) epoxides. We have determined the X‐ray crystal structures of Lsd18 in the substrate‐free, substrate‐bound, and product‐bound forms. Our work has revealed that Lsd18 has an extra‐large substrate‐binding pocket that allows the polyene to adopt different conformations within the enzyme pocket. This feature enables Lsd18 to epoxidate both of the C═C groups. Additionally, a subpocket located near the Lsd18 active site controls stereoselectivity by dictating which face of the C═C group is placed next to the flavin. Molecular understanding of how Lsd18 transforms a polyene into a bisepoxide during lasalocid A biosynthesis lays the foundation for the production of designer polyethers for drug development.

Multi-omics analysis and single-cell sequencing revealed the lysosome associated molecular subtypes and prognostic model development of papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common endocrine carcinoma in recent years, necessitating more precise risk stratification to accurately identify low-risk patients. Although preliminary evidence exists, studies on lysosomes in PTC are limited. This study utilized multi-omics data from the TCGA database to comprehensively investigate the genomic and biological characteristics of lysosomes in PTC patients and identify lysosome-associated genes (LAGs) linked to PTC prognosis. We developed a LAG scoring system for risk stratification based on the expression levels of risk coefficients and independent prognostic LAG variables. Clinical value was assessed through immune infiltration analysis, pathological subgroup analysis, immunotherapy response, and drug sensitivity prediction. Single-cell sequencing from the GEO database was used to analyze PTC samples, and bioinformatics findings were validated using western blot, qRT-PCR, colony formation, and Transwell assays. A new LAG scoring system was developed based on five prognostic LAGs, with single-cell sequencing revealing their expression in different cell types. The role of one LAG, DNASE2B, in PTC cell cloning, proliferation, and invasion was further confirmed in vitro. This comprehensive study highlights the complex interactions between lysosomes and PTC biology, offering new insights into the role of lysosomes in PTC and identifying potential targets for intervention.

New Transferrin Receptor‐Targeting Conjugate Effectively Delivers DNA to Mouse Brain

Abstract Delivery across the blood–brain barrier (BBB) is one of the most challenging tasks for modern biopharmaceutics. Many attempts have been taken, with only low delivery efficacies achieved so far. We report a new transferrin receptor‐targeting (TfR) RNA aptamer conjugated to DSPE lipid that leads to an unprecedented effective uptake in the brain, with brain‐to‐serum ratios up to 6.5 in mice. This result is superior to recently published values of < 1 for antibody conjugates and nanovesicles, pointing to a successful combined effect of the increased lipophilicity and TfR targeting with the new RNA aptamer that our conjugate provides. Using fluorescence whole body imaging, polymerase‐chain reaction (PCR) and fluorescence in situ hybridization, we confirm that the new conjugate delivers high amounts of DNA oligonucleotide to brains of Balb/cJ mice, and it is effective in human cells. There is no acute toxicity as verified with histopathological assessment of mice organs. The combination of properties demonstrated by our new conjugate makes it a highly potent delivery tool that can be applied in therapy of brain diseases incl. glioblastoma, neurogenerative diseases, and a broad range of brain infections.