Hasil untuk "Pharmacy and materia medica"

Eimear Byrne, Giovanni Longobardi, and Rocco Trombetti

In this article, we study polymatroids that are representable by means of linear restricted rank-metric codes, namely, by subspaces of the space of alternating, symmetric, or Hermitian square matrices endowed with the rank metric. More precisely, we characterize the rank function defining these polymatroids and establish sufficient conditions on the relevant parameters under which it is fully determined. We show that there are several differences in compared to the behaviour of $q$-polymatroids of unrestricted matrix codes.

Khalid Adnan Alsayed

Artificial intelligence (AI) systems are increasingly integrated into healthcare and pharmacy workflows, supporting tasks such as medication recommendations, dosage determination, and drug interaction detection. While these systems often demonstrate strong performance under standard evaluation metrics, their reliability in real-world decision-making remains insufficiently understood. In high-risk domains such as medication management, even a single incorrect recommendation can result in severe patient harm. This paper examines the reliability of AI-assisted medication systems by focusing on system failures and their potential clinical consequences. Rather than evaluating performance solely through aggregate metrics, this work shifts attention towards how errors occur and what happens when AI systems produce incorrect outputs. Through a series of controlled, simulated scenarios involving drug interactions and dosage decisions, we analyse different types of system failures, including missed interactions, incorrect risk flagging, and inappropriate dosage recommendations. The findings highlight that AI errors in medication-related contexts can lead to adverse drug reactions, ineffective treatment, or delayed care, particularly when systems are used without sufficient human oversight. Furthermore, the paper discusses the risks of over-reliance on AI recommendations and the challenges posed by limited transparency in decision-making processes. This work contributes a reliability-focused perspective on AI evaluation in healthcare, emphasising the importance of understanding failure behavior and real-world impact. It highlights the need to complement traditional performance metrics with risk-aware evaluation approaches, particularly in safety-critical domains such as pharmacy practice.

Daeyeong Lee, Sooa Lim

ILs have emerged as versatile formulation components in DDS due to their tunable physicochemical properties and ability to modulate biomolecular and interfacial interactions. This review examines IL-enabled DDS strategies across major delivery platforms, including nanocarrier-based systems, microtechnology-assisted devices, and biomacromolecule formulations, with emphasis on formulation design principles rather than administration route. We discuss how ILs enhance API solubility, stability, permeability, and formulation flexibility through API–IL complex formation and controlled membrane interactions and relate mechanistic insights into IL–membrane interactions to both delivery performance and safety via structure–activity relationships. Current limitations, including toxicity concerns, lack of standardized evaluation criteria, scalability challenges, and regulatory ambiguity, are critically assessed. Overall, this review positions ILs as formulation-enabling materials rather than standalone therapeutics and underscores the importance of rational design, standardized assessment, and early regulatory alignment for advancing IL-enabled DDS toward clinical translation.

Minyang Chen, Chenchen Feng, and Ran Cheng

As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization problems. While the advantages of DE are well-recognized, achieving peak performance heavily depends on its hyperparameters such as the mutation factor, crossover probability, and the selection of specific DE strategies. Traditional approaches to this hyperparameter dilemma have leaned towards parameter tuning or adaptive mechanisms. However, identifying the optimal settings tailored for specific problems remains a persistent challenge. In response, we introduce MetaDE, an approach that evolves DE's intrinsic hyperparameters and strategies using DE itself at a meta-level. A pivotal aspect of MetaDE is a specialized parameterization technique, which endows it with the capability to dynamically modify DE's parameters and strategies throughout the evolutionary process. To augment computational efficiency, MetaDE incorporates a design that leverages parallel processing through a GPU-accelerated computing framework. Within such a framework, DE is not just a solver but also an optimizer for its own configurations, thus streamlining the process of hyperparameter optimization and problem-solving into a cohesive and automated workflow. Extensive evaluations on the CEC2022 benchmark suite demonstrate MetaDE's promising performance. Moreover, when applied to robot control via evolutionary reinforcement learning, MetaDE also demonstrates promising performance. The source code of MetaDE is publicly accessible at: https://github.com/EMI-Group/metade.

Sadegh Rajabi, Akram Shahhosseini, Mahboubeh Irani et al.

The metastasis process plays an important role in the outcome of all cancers, including breast cancer, a leading cause of cancer mortality in women. This study assessed the effects of gaillardin on the metastatic activity of two different breast cancer cell lines. The MTT assay was used to obtain the IC50 concentrations. Migration or metastatic capability of MCF7 and MDA-MB231 cell lines was assayed using the wound scratch assay. The real-time PCR was utilized to quantify the gene expression of epithelial-mesenchymal transition (EMT) markers CDH1, CDH2, VIM, and FN1, along with angiogenesis-related markers VEGFA and THBS1. Western blotting was conducted to estimate the protein expression of E-cadherin, N-cadherin, vimentin, fibronectin 1, VEGFA, and thrombospondin 1. Treatment of the MCF7 cell line with different concentrations of gaillardin revealed no significant effect on the metastatic capacity of these cancer cells compared with the controls. However, the migratory activity and aggressiveness of MDA-MB231 cells were significantly hindered compared to the control cells. The results of gene expression data revealed the upregulating effect of gaillardin on the expression of CDH1 and THBS1 genes. Conversely, this phytochemical significantly downregulated CDH2, VIM, FN1, and VEGFA transcripts. Western blotting results showed a similar effect of gaillardin on the expression levels of the above-mentioned markers. The present data highlight the anti-metastatic activity of gaillardin in breast cancer in a receptor-independent manner. These results also indicate gaillardin as a potential anti-metastatic natural compound against triple-negative breast cancer cells, via two mechanisms that act by suppressing EMT and angiogenesis.

Ya Zhou, Feixia Wang, Mengru Hu et al.

<b>Background:</b> Hepatic fibrosis (HF) is a progressive liver disease characterized by the activation of hepatic stellate cells (HSCs) and changes in lipid metabolism. Abnormal ketone body (KD) levels, including acetoacetate (AcAc) and beta-hydroxybutyrate (BHB), have been observed in patients with HF, but the mechanisms linking ketone metabolism to fibrosis progression remain unclear. <b>Objectives:</b> This study aimed to investigate the role of AcAc in modulating HSCs activation and its potential mechanisms in HF. <b>Methods:</b> We examined the effects of AcAc on HSCs activation by Western blot analysis and RT-PCR both in vivo and in vitro. The impact of AcAc on lipid droplet accumulation in HSCs was assessed using total cholesterol (TC), triglyceride (TG), and Retinol (RET) kits, along with Nile Red and Oil Red O staining. RT-PCR screening was performed to analyze the expression of genes involved in lipid droplet formation and lipid metabolism. <b>Results:</b> Our findings show that AcAc inhibited HSCs activation by restoring LD levels. Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) was identified as a key regulator through gene screening. AcAc primarily regulated PPARγ expression, and knocking down PPARγ significantly aggravated HF progression. <b>Conclusions:</b> The ability of AcAc to restore LD levels and regulate PPARγ suggests that it may represent a promising therapeutic strategy for HF by inhibiting HSCs activation.

Anabel Franco-Moreno, Elena Madroñal-Cerezo, Ana Martínez-Casa-Muñoz et al.

Direct oral anticoagulants (DOACs) have emerged as the preferred oral anticoagulant therapy for patients with deep vein thrombosis of the lower extremities and pulmonary embolism. DOACs offer several advantages over vitamin K antagonists, including fixed dosage, fewer drug interactions, faster onset of action, and a lower risk of major bleeding, especially intracranial. Although evidence on the use of DOACs in unusual-site venous thrombosis (USVT) is limited, their use in such cases is becoming increasingly common. This narrative review examines the evidence derived from randomized controlled trials, and large observational studies focused on the use of the DOACs in USVT, including cerebral, splanchnic, upper extremity, ovarian, renal, and retinal vein thrombosis. In addition, it also provides practical advice for their use in these clinical settings according to the updated scientific literature.

Georgi H. Spasov, Riccardo Rossi, Andrea Vanossi et al.

The reliability and accuracy of numerical models and computer simulations to study aerosol deposition in the human respiratory system is investigated for a patient-specific tracheobronchial tree geometry. A computational fluid dynamics (CFD) model coupled with discrete elements methods (DEM) is used to predict the transport and deposition of the aerosol. The results are compared to experimental and numerical data available in the literature to study and quantify the impact of the modeling parameters and numerical assumptions. Even if the total deposition compares very well with the reference data, it is clear from the present work how local deposition results can depend significantly upon spatial discretization and boundary conditions adopted to represent the respiratory act. The modeling of turbulent fluctuations in the airflow is also found to impact the local deposition and, to a minor extent, the flow characteristics at the inlet of the computational domain. Using the CFD-DEM model, it was also possible to calculate the airflow and particles splitting at bifurcations, which were found to depart from the assumption of being equally distributed among branches adopted by some of the simplified deposition models. The results thus suggest the need for further studies towards improving the quantitative prediction of aerosol transport and deposition in the human airways.

Wulin Liang, Mingqian Zhang, Jiahui Gao et al.

Safflower yellow is an extract of the famous Chinese medicine <i>Carthamus tinctorious</i> L, and safflower yellow injection (SYI) is widely used clinically to treat angina pectoris. However, there are few studies on the anti-myocardial ischemia/reperfusion (I/R) injury effect of SYI, and its mechanisms are unclear. In the present study, we aimed to investigate the protective effect of SYI on myocardial I/R injury and explore its underlying mechanisms. Male Sprague Dawley rats were randomly divided into a control group, sham group, model group, and SYI group (20 mg/kg, femoral vein injection 1 h before modeling). The left anterior descending coronary artery was ligated to establish a myocardial I/R model. H9c2 cells were exposed to oxygen–glucose deprivation/reoxygenation (OGD/R) after incubation with 80 μg/mL SYI for 24 h. In vivo, TsTC, HE, and TUNEL staining were performed to evaluate myocardial injury and apoptosis. A kit was used to detect superoxide dismutase (SOD) and malondialdehyde (MDA) to assess oxidative stress. In vitro, flow cytometry was used to detect the reactive oxygen species (ROS) content and apoptosis rate. Protein levels were determined via Western blotting. Pretreatment with SYI significantly reduced infarct size and pathological damage in rat hearts and suppressed cardiomyocyte apoptosis in vivo and in vitro. In addition, SYI inhibited oxidative stress by increasing SOD activity and decreasing MDA content and ROS production. Myocardial I/R and OGD/R activate endoplasmic reticulum (ER) stress, as evidenced by increased expression of activating transcription factor 6 (ATF6), glucose-regulated protein 78 (GRP78), cysteinyl aspartate-specific proteinase caspase-12, and C/EBP-homologous protein (CHOP), which were all inhibited by SYI. SYI ameliorated myocardial I/R injury by attenuating apoptosis, oxidative damage, and ER stress, which revealed new mechanistic insights into its application.

Binghao Zhang, Aaron Babier, Timothy C. Y. Chan et al.

Purpose: To develop a machine learning-based, 3D dose prediction methodology for Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of any number, size, and shape. Methods: Data from 322 GK treatment plans was modified by isolating and cropping the contoured MRI and clinical dose distributions based on tumor location, then scaling the resulting tumor spaces to a standard size. An accompanying 3D tensor was created for each instance to account for tumor size. The modified dataset for 272 patients was used to train both a generative adversarial network (GAN-GK) and a 3D U-Net model (U-Net-GK). Unmodified data was used to train equivalent baseline models. All models were used to predict the dose distribution of 50 out-of-sample patients. Prediction accuracy was evaluated using gamma, with criteria of 4%/2mm, 3%/3mm, 3%/1mm and 1%/1mm. Prediction quality was assessed using coverage, selectivity, and conformity indices. Results: The predictions resulting from GAN-GK and U-Net-GK were similar to their clinical counterparts, with average gamma (4%/2mm) passing rates of 84.9 and 83.1, respectively. In contrast, the gamma passing rate of baseline models were significantly worse than their respective GK-specific models (p < 0.001) at all criterion levels. The quality of GK-specific predictions was also similar to that of clinical plans. Conclusion: Deep learning models can use GK-specific data modification to predict 3D dose distributions for GKRS plans with a large range in size, shape, or number of targets. Standard deep learning models applied to unmodified GK data generated poorer predictions.

Marwan Algellay, Matthew Roberts, Lucy Bosworth et al.

Three-dimensional printing (3DP) allows production of novel fast dissolving oral films (FDFs). However, mechanical properties of the films may not be desirable when certain excipients are used. This work investigated whether adding chitosan micro-ribbons or cellulose microfibres will achieve desired FDFs by fused deposition modelling 3DP. Filaments containing polyvinyl alcohol (PVA) and paracetamol as model drug were manufactured at 170 °C. At 130 °C, filaments containing polyvinylpyrrolidone (PVP) and paracetamol were also created. FDFs were printed with plain or mesh patterns at temperatures of 200 °C (PVA) or 180 °C (PVP). Both chitosan micro-ribbons and cellulose micro-fibres improved filament mechanical properties at 1% <i>w</i>/<i>w</i> concentration in terms of flexibility and stiffness. The filaments were not suitable for printing at higher concentrations of chitosan micro-ribbons and cellulose micro-fibres. Furthermore, mesh FDFs containing only 1% chitosan micro-ribbons disintegrated in distilled water within 40.33 ± 4.64 s, while mesh FDFs containing only 7% croscarmellose disintegrated in 55.33 ± 2.86 s, and croscarmellose containing films showed signs of excipient scorching for PVA polymer. Cellulose micro-fibres delayed disintegration of PVA mesh films to 108.66 ± 3.68 s at 1% <i>w</i>/<i>w</i>. In conclusion, only chitosan micro-ribbons created a network of hydrophilic channels within the films, which allowed faster disintegration time at considerably lower concentrations.

Matthias Otto, Yannik Kropp, Evelyn Jäger et al.

Mechanically ventilated patients suffering from acute respiratory distress syndrome (ARDS) frequently receive aerosolized iloprost. Because of prostacyclin’s short half-life, prolonged inhalative administration might improve its clinical efficacy. But, this is technically challenging. A solution might be the use of inspiration-synchronized vibrating mesh nebulizers (VMN_syn), which achieve high drug deposition rates while showing prolonged nebulization times. However, there are no data comparing prolonged to bolus iloprost nebulization using a continuous vibrating mesh nebulizer (VMN_cont) and investigating the effects of different ventilation modes on inspiration-synchronized nebulization. Therefore, in an in vitro model of mechanically ventilated adults, a VMN_syn and a VMN_cont were compared in volume-controlled (VC-CMV) and pressure-controlled continuous mandatory ventilation (PC-CMV) regarding iloprost deposition rate and nebulization time. During VC-CMV, the deposition rate of the VMN_syn was comparable to the rate obtained with the VMN_cont, but 10.9% lower during PC-CMV. The aerosol output of the VMN_syn during both ventilation modes was significantly lower compared to the VMN_cont, leading to a 7.5 times longer nebulization time during VC-CMV and only to a 4.2 times longer nebulization time during PC-CMV. Inspiration-synchronized nebulization during VC-CMV mode therefore seems to be the most suitable for prolonged inhalative iloprost administration in mechanically ventilated patients.

Niharika Patel, Zeeshan Heera Ahmad, Shahzad Ali Shah et al.

Objectives: This research was done to evaluate how the root canal taper affects the Endodontically Treated Teeth (ETT) prepared with the TruNatomy and Protaper Next file systems in terms of fracture resistance. Materials and Method: Forty recently extracted mandibular premolar teeth were used in this research, which was classified into four groups at random. Groups 1a and 1b used TruNatomy 4% and 6%, respectively, while groups 2a and 2b used the Protaper Next 4% and 6% file systems, respectively. The root canals were cleaned, shaped, and sealed using cold lateral compaction. The root canals were then fixed in standardized autopolymerizing acrylic resin blocks and tested for vertical root fracture using a universal testing machine. Newtons were used to measure the forces needed to cause fractures. Data were statistically analyzed. Results: In comparison with other groups, group 1a (TruNatomy 4%) displayed greater fracture resistance (423.322.43 Newtons), and group 2b (Protaper Next 6%) displayed the least fracture resistance (264.512.76 Newtons). Conclusion: Protaper Next file system had lower fracture resistance than TruNatomy file system. With the use of greater taper instruments, a notable decrease in the fracture resistance of ETT was observed.

Adel Albshri, Bakri Awaji, and Ellis Solaiman

The pervasiveness of the Internet of Things (IoT) has enabled the administration of a large number of intelligent devices. However, IoT is based on centralised models, which introduce a number of problems, such as a single point of failure and security risks. Blockchain may offer a viable option for addressing these concerns. Practically, both blockchain and IoT are complex technologies posing further challenges in assessing application performance. The availability of a reliable simulation environment for Blockchain based IoT applications would be a major aid in the development and evaluation of such applications. Our research has found that currently there are no simulators with a comprehensive set of features, for the development and evaluation of blockchain based IoT applications, which is the main motivation for our work. The purpose of this study is to gather the opinions of experts regarding the creation of a simulation environment for IoT based blockchain applications. To do this, we utilise two separate investigations. First, a questionnaire is developed to ensure that the development of such simulation software would be of significant use. Second, interviews with participants are performed to gain their perspectives on the primary issues they face with blockchain-based IoT applications. In addition, the interviews focused on collecting the perspectives of participants on how blockchain may improve IoT and how to identify blockchain's applicability in IoT. Our findings demonstrate that the participants had a great deal of confidence in blockchain to resolve IoT issues. However, they lack the tools necessary to assess this concept. This highlights their requirement for a simulator to analyse the integration of blockchain and IoT.

Dzenefa Alihodzic, Sebastian G. Wicha, Otto R. Frey et al.

Extracorporeal membrane oxygenation (ECMO) is utilized to temporarily sustain respiratory and/or cardiac function in critically ill patients. Ciprofloxacin is used to treat nosocomial infections, but data describing the effect of ECMO on its pharmacokinetics is lacking. Therefore, a prospective, observational trial including critically ill adults (<i>n</i> = 17), treated with ciprofloxacin (400 mg 8–12 hourly) during ECMO, was performed. Serial blood samples were collected to determine ciprofloxacin concentrations to assess their pharmacokinetics. The pharmacometric modeling was performed (NONMEM^®) and utilized for simulations to evaluate the probability of target attainment (PTA) to achieve an AUC_0–24/MIC of 125 mg·h/L for ciprofloxacin. A two-compartment model most adequately described the concentration-time data of ciprofloxacin. Significant covariates on ciprofloxacin clearance (CL) were plasma bicarbonate and the estimated glomerular filtration rate (eGFR). For pathogens with an MIC of ≤0.25 mg/L, a PTA of ≥90% was attained. However, for pathogens with an MIC of ≥0.5 mg/L, plasma bicarbonate ≥ 22 mmol/L or eGFR ≥ 10 mL/min PTA decreased below 90%, steadily declining to 7.3% (plasma bicarbonate 39 mmol/L) and 21.4% (eGFR 150 mL/min), respectively. To reach PTAs of ≥90% for pathogens with MICs ≥ 0.5 mg/L, optimized dosing regimens may be required.

Maria Sanz Codina, Milo Gatti, Carla Troisi et al.

Objectives: The objective of this study was to explore the relationship between pharmacokinetic/pharmacodynamic (PK/PD) target attainment of continuous-infusion (CI) meropenem and microbiological outcome in critical COVID-19 patients with documented Gram-negative superinfections. Methods: Patients receiving CI meropenem for documented Gram-negative infections at the COVID ICU of the IRCCS Azienda Ospedaliero-Universitaria di Bologna and undergoing therapeutic drug monitoring from January 2021 to February 2022 were retrospectively assessed. Average steady-state meropenem concentrations (C_ss) were calculated and the C_ss/MIC ratio was selected as a pharmacodynamic parameter of meropenem efficacy. The C_ss/MIC ratio was defined as optimal if ≥4, quasi-optimal if between 1 and 4, and suboptimal if <1. The relationship between C_ss/MIC and microbiological outcome was assessed. Results: Overall, 43 critical COVID-19 patients with documented Gram-negative infections were retrieved. Combination therapy was implemented in 26 cases. C_ss/MIC ratios were optimal in 27 (62.8%), quasi-optimal in 7 (16.3%), and suboptimal in 9 cases (20.9%). Microbiological failure occurred in 21 patients (48.8%), with no difference between monotherapy and combination therapy (43.8% vs. 53.8%; <i>p</i> = 0.53). The microbiological failure rate was significantly lower in patients with an optimal C_ss/MIC ratio compared to those with a quasi-optimal or suboptimal C_ss/MIC ratio (33.3% vs. 75.0%; <i>p</i> = 0.01). Conclusion: Suboptimal attainment of meropenem PK/PD targets may be a major determinant impacting on microbiological failure in critical COVID-19 patients with Gram-negative superinfections.

Chanchal Katariya, Arvina Rajasekar

Microsurgery is a minimally invasive procedure that uses a surgical microscope, specially designed equipment, and suture materials. Even though this equipment and expertise of numerous surgeries are required to meet patient esthetic reckoning, doctors must be ready to invest time and effort into becoming familiar with novel surgical methods and devices. The ambition of this case series is to compare conventional macro surgery and microsurgery in terms of clinical approach. This study included four cases, two flap surgery, and two root coverage. Clinical parameters for root coverage, increase in keratinized tissue (KT), gain in clinical attachment level (CAL) and complete root coverage (CRC), dentin hypersensitivity index-Schiff's index and for flap surgery, probing depth, clinical attachment level. Healing and pain analysis were done. There was no significant difference seen between conventional and clinical outcomes of a microsurgical technique such as clinical attachment level, probing depth, increase in KT, gain in clinical attachment level (CAG), and CRC, dentin hypersensitivity index-Schiff's index. When patient-based outcomes such as healing index and Visual Analog Scale, a significant difference was seen. If a microsurgical method is used instead of a traditional macroscopic approach, the early healing index can be significantly improved and there will be less postoperative pain.

Huagu Ye

Qi Bao, Miao-Kun Wang, AND Song-Liang Qiu

The authors establish the necessary and sufficient conditions under which certain combinations of Gaussian hypergeometric function and elementary function are monotone in the parameter, which generalize the recent results of generalized elliptic integrals of the first and second kinds obtained by Qiu et al. Moreover, the authors also prove two monotonicity theorems of generalized elliptic integrals from another point of view.

Nils Fleischhacker, Kasper Green Larsen, and Mark Simkin

Property-preserving hash functions allow for compressing long inputs $x_0$ and $x_1$ into short hashes $h(x_0)$ and $h(x_1)$ in a manner that allows for computing a predicate $P(x_0, x_1)$ given only the two hash values without having access to the original data. Such hash functions are said to be adversarially robust if an adversary that gets to pick $x_0$ and $x_1$ after the hash function has been sampled, cannot find inputs for which the predicate evaluated on the hash values outputs the incorrect result. In this work we construct robust property-preserving hash functions for the hamming-distance predicate which distinguishes inputs with a hamming distance at least some threshold $t$ from those with distance less than $t$. The security of the construction is based on standard lattice hardness assumptions. Our construction has several advantages over the best known previous construction by Fleischhacker and Simkin. Our construction relies on a single well-studied hardness assumption from lattice cryptography whereas the previous work relied on a newly introduced family of computational hardness assumptions. In terms of computational effort, our construction only requires a small number of modular additions per input bit, whereas previously several exponentiations per bit as well as the interpolation and evaluation of high-degree polynomials over large fields were required. An additional benefit of our construction is that the description of the hash function can be compressed to $λ$ bits assuming a random oracle. Previous work has descriptions of length $\mathcal{O}(\ell λ)$ bits for input bit-length $\ell$, which has a secret structure and thus cannot be compressed. We prove a lower bound on the output size of any property-preserving hash function for the hamming distance predicate. The bound shows that the size of our hash value is not far from optimal.