Magnetic Resonance Spectra Research Articles

Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer

Metabolomics has emerged as a powerful tool for identifying biomarkers of disease, and nuclear magnetic resonance (NMR) spectroscopy allows for the simultaneous detection of a wide range of metabolites. However, due to complex interactions within metabolic networks, metabolites often exhibit high correlation and collinearity. To address this challenge, self-organizing maps (SOMs) of Kohonen maps and counter propagation-artificial neural networks (CP-ANN) were employed in this study to model proton nuclear magnetic resonance spectroscopic (1HNMR) data from control samples and breast cancer (BC) patients. Blood serum samples from a control group (n=24) and BC patients (n=18) were used to extract metabolites using methanol and chloroform solvents in optimum extraction conditions. The 1HNMR data was preprocessed by performing phase, baseline, and shift corrections. Subsequently, the preprocessed data was modeled using Kohonen network as an unsupervised technique and CP-ANN as a supervised technique. In this regard, the model built with CP-ANN successfully distinguished between the two classes with an accuracy of 100 % for both group and sensitivity of 96 % and 100 % for control group and BC patients, respectively. Additionally, CP-ANN algorithm demonstrated predictive capabilities by accurately classifying test samples with 90 % sensitivity, 98 % specificity, and 96 % accuracy for control group and 100 % sensitivity, 90 % specificity, and 96 % accuracy for BC patients. Furthermore, analysis of the resulting topological map revealed 14 significant variables (biomarkers) such as sarcosine, lysine, trehalose, tryptophan, and betaine that effectively differentiated between healthy individuals and BC patients.

Effect of Dynamical Motion in ab Initio Calculations of Solid-State Nuclear Magnetic and Nuclear Quadrupole Resonance Spectra.

Solid-state nuclear magnetic resonance (SSNMR) and nuclear quadrupole resonance (NQR) spectra provide detailed information about the electronic and atomic structure of solids. Modern ab initio methods such as density functional theory (DFT) can be used to calculate NMR and NQR spectra from first-principles, providing a meaningful avenue to connect theory and experiment. Prediction of SSNMR and NQR spectra from DFT relies on accurate calculation of the electric field gradient (EFG) tensor associated with the potential of electrons at the nuclear centers. While static calculations of EFGs are commonly seen in the literature, the effects of dynamical motion of atoms in molecules and solids have been less explored. In this study, we develop a method to calculate EFGs of solids while taking into account the dynamics of atoms through DFT-based molecular dynamics simulations. The method we develop is general, in the sense that it can be applied to any material at any desired temperature and pressure. Here, we focus on application of the method to NaNO2 and study in detail the EFGs of 14N, 17O, and 23Na. We find in the cases of 14N and 17O that the dynamical motion of the atoms can be used to calculate mean EFGs that are in closer agreement with experiments than those of static calculations. For 23Na, we find a complex behavior of the EFGs when atomic motion is incorporated that is not at all captured in static calculations. In particular, we find a distribution of EFGs that is influenced strongly by the local (changing) bond environment, with a pattern that reflects the coordination structure of 23Na. We expect the methodology developed here to provide a path forward for understanding materials in which static EFG calculations do not align with experiments.

Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events

BackgroundThe cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters. ObjectivesThis study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM. MethodsA total of 758 patients with HCM (67% male; age 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation–related stroke. ResultsMACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, −17.3% for GCS). ConclusionsML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.

Enhancing Chemical Reaction Monitoring with a Deep Learning Model for NMR Spectra Image Matching to Target Compounds.

In the synthetic laboratory, researchers typically rely on nuclear magnetic resonance (NMR) spectra to elucidate structures of synthesized products and confirm whether they match the desired target compounds. As chemical synthesis technology evolves toward intelligence and continuity, efficient computer-assisted structure elucidation (CASE) techniques are required to replace time-consuming manual analysis and provide the necessary speed. However, current CASE methods typically aim to derive precise chemical structures from spectroscopic data, yet they suffer from drawbacks such as low accuracy, high computational cost, and reliance on chemical libraries. In meticulously designed chemical synthesis reactions, researchers prioritize confirming the attainment of the target product based on NMR spectra, rather than focusing on identifying the specific product obtained. For this purpose, we innovatively developed a binary classification model, termed as MatCS, to directly predict the relationship between NMR spectra image (including 1H NMR and 13C NMR) and the molecular structure of the target compound. After evaluating various feature extraction methods, MatCS employs a combination of the Graph Attention Networks and Graph Convolutional Networks to learn the structural features of molecular graphs and the pretrained ResNet101 network with a Convolutional Block Attention Module to extract features from NMR spectra images. The results show that on a challenging Testsim data set, which poses difficulty in distinguishing spectra of similar molecular structures, MatCS achieves comprehensive evaluation metrics with an F1-score of 0.81 and an AUC value of 0.87. Simultaneously, it exhibited commendable performance on an external SDBS data set containing experimental NMR spectra, showcasing substantial potential for structural verification tasks in real automated chemical synthesis.

Quantifying the water saturation degree of cement-based materials by hydrogen nuclear magnetic resonance (1H NMR)

This study investigated the T2 spectrum of hydrogen nuclear magnetic resonance (1H NMR) signals in cement paste with water-to-cement ratios (w/c) of 0.2, 0.35, and 0.5 under three different saturation methods (natural soak, vacuum saturation, and vacuum saturation with high pressure). The saturation degree and pore structure of the cement paste under different saturation methods has been calculated through the linear relationship between water absorption and increment of NMR amplitude intensity. The results showed that the saturation degree of the cement paste increased progressively with the duration of saturation until it reached a state of equilibrium. It can increase saturation degree and reduce the saturation time by increasing the saturation pressure, and vacuum saturation with high pressure is necessary for cement paste with lower w/c. It is important to noted that conditions such as unsaturation, natural soak, and vacuum saturation may result in incomplete water saturation, leading to a partial loss of the 1H NMR signal amplitude intensity and underestimation of porosity measurements. Considering the duration of saturation and the degree of saturation, it is recommended a saturation duration of at least 4 h with a vacuum pressure of 8 MPa for cement paste with w/c ratio of 0.2 and 0.35. For cement paste with w/c of 0.5, a saturation duration of at least 0.5 h with a vacuum pressure of 8 MPa is suggested.

Can Graph Machines Accurately Estimate 13C NMR Chemical Shifts of Benzenic Compounds?

In the organic laboratory, the 13C nuclear magnetic resonance (NMR) spectrum of a newly synthesized compound remains an essential step in elucidating its structure. For the chemist, the interpretation of such a spectrum, which is a set of chemical-shift values, is made easier if he/she has a tool capable of predicting with sufficient accuracy the carbon-shift values from the structure he/she intends to prepare. As there are few open-source methods for accurately estimating this property, we applied our graph-machine approach to build models capable of predicting the chemical shifts of carbons. For this study, we focused on benzene compounds, building an optimized model derived from training a database of 10,577 chemical shifts originating from 2026 structures that contain up to ten types of non-carbon atoms, namely H, O, N, S, P, Si, and halogens. It provides a training root-mean-squared relative error (RMSRE) of 0.5%, i.e., a root-mean-squared error (RMSE) of 0.6 ppm, and a mean absolute error (MAE) of 0.4 ppm for estimating the chemical shifts of the 10k carbons. The predictive capability of the graph-machine model is also compared with that of three commercial packages on a dataset of 171 original benzenic structures (1012 chemical shifts). The graph-machine model proves to be very efficient in predicting chemical shifts, with an RMSE of 0.9 ppm, and compares favorably with the RMSEs of 3.4, 1.8, and 1.9 ppm computed with the ChemDraw v. 23.1.1.3, ACD v. 11.01, and MestReNova v. 15.0.1-35756 packages respectively. Finally, a Docker-based tool is proposed to predict the carbon chemical shifts of benzenic compounds solely from their SMILES codes.

Exploring the role of neutral ligands in modulating the photoluminescence of samarium complexes with 1,1,1,5,5,5-hexafluoro-2,4-pentanedione.

Four eight-coordinated luminescent samarium complexes of type [Sm(hfpd)3L2] and [Sm(hfpd)3L'] [where hfpd = 1,1,1,5,5,5-Hexafluoro-2,4-pentanedione L = tri-octyl-phosphine oxide (TOPO) and L' = 1,10-phenanthroline (phen), neocuproine (neoc) and bathocuproine (bathoc) were synthesized via a stoichiometrically controlled approach. This allows for precise control over the stoichiometry of the complexes, leading to reproducible properties. This investigation focuses on understanding the impact of secondary ligands on the luminescent properties of these complexes. Infrared (IR) spectra provided information about the molecular structures, whereas 1H and 13C nuclear magnetic resonance (NMR) spectra confirmed these structural details along with the coordination of ligands to trivalent Sm ion. The UV-vis spectra revealed the molar absorption coefficient and absorption bands associated with the hfpd ligand and photoluminescence (PL) spectroscopy demonstrated intense orange-red emission (648 nm relative to 4G5/2 → 6H9/2) from the complexes. The Commission Internationale de l'Éclairage (CIE) triangles indicated that the complexes emitted warm orange red light with color coordinates (x, y) ranging from (0.62, 0.36) to (0.40, 0.27). The investigation of the band gap as well as color parameters confirms the utility of these complexes in displays and LEDs.

Structural analysis of Salvia miltiorrhiza polysaccharide and its regulatory functions on T cells subsets in tumor-bearing mice combined with thymopentin

Salvia miltiorrhiza ethanol-extracted polysaccharide (SMEP) and thymopentin (TP5) have been proved with strong immunomodulatory activity, and T cells subsets play pivotal roles in the inhibition of solid tumors growth. In the present study, the structure of SMEP was further identified via methylation and nuclear magnetic resonance spectra, and the immunomodulatory activity in combination with TP5 was investigated via evaluating T cell subsets spatial distributions in tumor-bearing mice, finally the cellular status of solid tumor cells was analyzed. The results revealed that SMEP was a neutral heteropolysaccharide using (1 → 4)-α-D-Glcp and (2 → 1)-β-D-Fruf as the main chain, along with branched chains of (1 → 6)-α-D-Galp. The SMEP+TP5 treatments could effectively promote the differentiation and improve the specific recognition capacity of CD4+ T cells in tumor-bearing mice, thereby activate tumor-infiltrating CD8+ T cells to exert cytotoxic effects, finally promoting the tumor cells apoptosis via blocking cell cycle at G0/G1 phase, which might be relevant with suppression of Wnt/β-catenin signaling pathway. These findings highlighted the potential of SMEP as an immunoadjuvant for patients bearing immune-deficiency related diseases, and provided data support for the functional researches of T cell subsets in tumor immunity.

طريقه جديدة لتخليق مشتقات البايروزول - ثايازول مع دراسة نشاط مضادات الميكروبات

In this contribution, new derivatives of thiosemicarbazon, cyclic thiazolidinone, yilidine thiazolidinone, and parazolo-thiazole were synthesized, starting from the reaction of 2 -aminobenzothiazole with p-bromo phenacyl bromide to give the first step of 2(4-bromo phenyl) imidazole (2,1 -b) benzo thiazole (1). Compound (1) then was subjected under Viels Myer Haack to yield 3-carbaldehyde linking with imidazo-benzothiazol (2). Compound (2) condensed with thiosemicarbazide togivenewsynthesis of thiosemicarbazon derivative (3). Compound 4-Oxo-1,3-thiazolidine (4) was constituted from reacting compound (3) with ethyl chloroacetate, while Thiazolidinone derivative (5) was formed by reacting compound (4) with aromatic aldehyde to give 4-Oxo-1,3-thiazolidine-5 benzylidene linking with imidazo-benzothiazol (5). Compound (5) was then reacted with hydrazine hydride to give fused ring of pyrazolo thaizolo derivatives (6). In the finally, azo methene groups were opening by two reagents are (acetic anhydride and 4-nitro benzoyl chloride) to give new derivatives of hydrazone contacted with imidazobenzothiazol (7) and (8) compound respectively. All the prepared compounds were identified by Fourier Transform Infrared (FT-IR), Proton nuclear magnetic resonance (1 H-NMR) and Carbon nuclear magnetic resonance (13C-NMR) spectra. Some of the synthesized compounds were evaluated via biological activity.

Brain effect mechanism of lever positioning manipulation on LDH analgesia based on multimodal MRI: a study protocol

IntroductionThe clinical symptoms of Lumbar Disc Herniation (LDH) can be effectively ameliorated through Lever Positioning Manipulation (LPM), which is closely linked to the brain's pain-regulating mechanisms. Magnetic Resonance Imaging (MRI) offers an objective and visual means to study how the brain orchestrates the characteristics of analgesic effects. From the perspective of multimodal MRI, we applied functional MRI (fMRI) and Magnetic Resonance Spectrum (MRS) techniques to comprehensively evaluate the characteristics of the effects of LPM on the brain region of LDH from the aspects of brain structure, brain function and brain metabolism. This multimodal MRI technique provides a biological basis for the clinical application of LPM in LDH.Methods and analysisA total of 60 LDH patients and 30 healthy controls, matched by gender, age, and years of education, will be enrolled in this study. The LDH patients will be divided into two groups (Group 1, n = 30; Group 2, n = 30) using a random number table method. Group 1 will receive LPM treatment once every two days, for a total of 12 times over 4 weeks. Group 2 will receive sham LPM treatment during the same period as Group 1. All 30 healthy controls will be divided into Group 3. Multimodal MRI will be performed on Group 1 and Group 2 at three time points (TPs): before LPM (TP1), after one LPM session (TP2), and after a full course of LPM treatment. The healthy controls (Group 3) will not undergo LPM and will be subject to only a single multimodal MRI scan. Participants in both Group 1 and Group 2 will be required to complete clinical questionnaires. These assessments will focus on pain intensity and functional disorders, using the Visual Analog Scale (VAS) and the Japanese Orthopaedic Association (JOA) scoring systems, respectively.DiscussionThe purpose of this study is to investigate the multimodal brain response characteristics of LDH patients after treatment with LPM, with the goal of providing a biological basis for clinical applications.Trial registration numberhttps://clinicaltrials.gov/ct2/show/NCT05613179, identifier: NCT05613179.

Simulating multipulse NMR spectra of polycrystalline solids in the frequency domain.

An approach is presented for simulating multipulse nuclear magnetic resonance (NMR) spectra of polycrystalline solids directly in the frequency domain. The approach integrates the symmetry pathway concept for multipulse NMR with efficient algorithms for calculating spinning sideband amplitudes and performing interpolated finite-element numerical integration over all crystallite orientations in a polycrystalline sample. The numerical efficiency is achieved through a set of assumptions used to approximate the evolution of a sparse density matrix through a pulse sequence as a set of individual transition pathway signals. The utility of this approach for simulating the spectra of complex materials, such as glasses and other structurally disordered materials, is demonstrated.

Mechanochemical Tailoring of Lignin Structure: Influence of Different Particle Sizes in the Organosolv Process.

The autocatalyzed ethanolic organosolv process is gaining increasing attention for the sulfur-free isolation of lignin, which is subsequently used as a renewable substitute for various fossil-based applications. For the first time, the mechanochemical influence of seven different particle sizes of two different biomasses on the respective organosolv lignin structure is examined. Wine pruning (Pinot Noir) and wine pomace (Accent) are used for organosolv process with particle sizes ranging from 2.0-1.6mm to less than 0.25mm. As particle size decreases, the weight-average molecular weight increases, while the total phenol content decreases significantly. Additionally, the distribution of the lignin-typical monolignols and relevant substructures, as determined by two-dimensional heteronuclear nuclear magnetic resonance spectra single quantum coherence (HSQC), is observed. The degree of grinding of the biomass has a clear chemical-structural influence on the isolated HG and HGS organosolv lignins. Therefore, it is crucial to understand this influence to apply organosolv lignins in a targeted manner. In the future, particle size specifications in the context of the organosolv process should be expressed in terms of distribution densities rather than in terms of a smaller than specification.

Talaroacids A-D and Talaromarane A, Diterpenoids with Anti-Inflammatory Activities from Mangrove Endophytic Fungus Talaromyces sp. JNQQJ-4.

Five new diterpenes including four diterpenes with 1,2,3,4,4a,5,6,8a-octalin skeleton talaroacids A-D (1-4) and an isopimarane diterpenoid talaromarane A (5) were isolated from the mangrove endophytic fungus Talaromyces sp. JNQQJ-4. Their structures and absolute configurations were determined by analysis of high-resolution electrospray ionization mass spectroscopy (HRESIMS), 1D/2D Nuclear Magnetic Resonance (NMR) spectra, single-crystal X-ray diffraction, quantum chemical calculation, and electronic circular dichroism (ECD). Talaromarane A (5) contains a rare 2-oxabicyclo [3.2.1] octan moiety in isopimarane diterpenoids. In bioassays, compounds 1, 2, 4, and 5 displayed significant anti-inflammatory activities with the IC50 value from 4.59 to 21.60 μM.

Real-Time Metabolic Magnetic Resonance Spectroscopy of Pancreatic and Colon Cancer Tumor-Xenografts with Parahydrogen Hyperpolarized 1-13C Pyruvate-d3.

Parahydrogen-induced polarization (PHIP) is an emerging technique to enhance the signal of stable isotope metabolic contrast agents for Magnetic Resonance (MR). The objective of this study is to continue establishing 1-13C-pyruvate-d3, signal-enhanced via PHIP, as a hyperpolarized contrast agent, obtained in seconds, to monitor metabolism in human cancer. Our focus was on human pancreatic and colon tumor xenografts. 1-13C-vinylpyruvate-d6 was hydrogenated using parahydrogen. Thereafter, the polarization of the protons was transferred to 13C. Following a workup procedure, the free hyperpolarized 1-13C-pyruvate-d3 was obtained in clean aqueous solution. After injection into animals bearing either pancreatic or colon cancer xenografts, slice-selective MR spectra were acquired and analyzed to determine rate constants of metabolic conversion into lactate and alanine. 1-13C-pyruvate-d3 proved to follow the increased metabolic rate to lactate and alanine in the tumor xenografts.

Explainable AI to Facilitate Understanding of Neural Network-Based Metabolite Profiling Using NMR Spectroscopy.

Neural networks (NNs) are emerging as a rapid and scalable method for quantifying metabolites directly from nuclear magnetic resonance (NMR) spectra, but the nonlinear nature of NNs precludes understanding of how a model makes predictions. This study implements an explainable artificial intelligence algorithm called integrated gradients (IG) to elucidate which regions of input spectra are the most important for the quantification of specific analytes. The approach is first validated in simulated mixture spectra of eight aqueous metabolites and then investigated in experimentally acquired lipid spectra of a reference standard mixture and a murine hepatic extract. The IG method revealed that, like a human spectroscopist, NNs recognize and quantify analytes based on an analyte's respective resonance line-shapes, amplitudes, and frequencies. NNs can compensate for peak overlap and prioritize specific resonances most important for concentration determination. Further, we show how modifying a NN training dataset can affect how a model makes decisions, and we provide examples of how this approach can be used to de-bug issues with model performance. Overall, results show that the IG technique facilitates a visual and quantitative understanding of how model inputs relate to model outputs, potentially making NNs a more attractive option for targeted and automated NMR-based metabolomics.

Ternary Rotational Polyanion Coupling Enables Fast Li Ion Dynamics in Tetrafluoroborate Ion Doped Antiperovskite Li2OHCl Solid Electrolyte

AbstractLi‐rich antiperovskite (LiRAP) hydroxyhalides are emerging as attractive solid electrolyte (SEs) for all‐solid‐state Li metal batteries (ASSLMBs) due to their low melting point, low cost, and ease of scaling‐up. The incorporation of rotational polyanions can reduce the activation energy and thus improve the Li ion conductivity of SEs. Herein, we propose a ternary rotational polyanion coupling strategy to fasten the Li ion conduction in tetrafluoroborate (BF4−) ion doped LiRAP Li2OHCl. Assisted by first‐principles calculation, powder X‐ray diffraction, solid‐state magnetic resonance and electrochemical impedance spectra, it is confirmed that Li ion transport in BF4− ion doped Li2OHCl is strongly associated with the rotational coupling among OH−, BF4− and Li2−O−H octahedrons, which enhances the Li ion conductivity for more than 1.8 times with the activation energy lowering 0.03 eV. This work provides a new perspective to design high‐performance superionic conductors with multi‐polyanions.

Ternary Rotational Polyanion Coupling Enables Fast Li Ion Dynamics in Tetrafluoroborate Ion Doped Antiperovskite Li2OHCl Solid Electrolyte.

Li-rich antiperovskite (LiRAP) hydroxyhalides are emerging as attractive solid electrolyte (SEs) for all-solid-state Li metal batteries (ASSLMBs) due to their low melting point, low cost, and ease of scaling-up. The incorporation of rotational polyanions can reduce the activation energy and thus improve the Li ion conductivity of SEs. Herein, we propose a ternary rotational polyanion coupling strategy to fasten the Li ion conduction in tetrafluoroborate (BF4-) ion doped LiRAP Li2OHCl. Assisted by first-principles calculation, powder X-ray diffraction, solid-state magnetic resonance and electrochemical impedance spectra, it is confirmed that Li ion transport in BF4- ion doped Li2OHCl is strongly associated with the rotational coupling among OH-, BF4- and Li2-O-H octahedrons, which enhances the Li ion conductivity for more than 1.8 times with the activation energy lowering 0.03 eV. This work provides a new perspective to design high-performance superionic conductors with multi-polyanions.

Synthesis, ADME Profiling, Antibacterial Screening and Molecular Docking of Some New Tetrazole‐Heterocycle Hybrids

AbstractOver the past 20 years, there has been a notable rise in the incidence of invasive bacterial infections. This increase has been mostly linked to the growth of drug‐resistant bacteria. Tetrazoles have been considered as promising antibacterial agents and their effectiveness may be enhanced by hybridization with other antibacterial pharmacophores. In this work, a series of tetrazole hybrids (1‐6) containing oxazepane and pyrazole rings as well as Mannich bases (7,8) were synthesized via the Schiff and Mannich reactions, respectively. The compositions were proven spectroscopically using infrared spectra, proton (1H) and carbon‐13 (13C) nuclear magnetic resonance spectra and elemental analyses. Moreover, the pharmacokinetic properties viz Absorption, Distribution, Metabolism and Excretion (ADME) were predicted in silico using SwissADME server. Compounds 1,2 and 4–6 attaining the best drug‐likeness properties were screened for their antibacterial activities against Staphylococcus epidermidis and Streptococcus mutans at different concentrations in comparison with tetracycline and amikacin, respectively. Afterwards, a molecular docking study was performed to explore the potential binding patterns of the new antibacterial compounds. Collectively, the tetrazole hybrids 4 and 5 have been found to have higher inhibitory potencies compared to tetracycline, serving them as potential antibacterial candidates which can be further optimized in the future.

Preparation and characterization of the injectable pH- and temperature-sensitive pentablock hydrogel containing human growth hormone-loaded chitosan nanoparticles via electrospraying

This research investigated the in vivo gelation, biodegradation, and drug release efficiency of a novel injectable sensitive drug delivery system for human growth hormone (HGh). This composite system comprises pH- and temperature-sensitive hydrogel, designated as oligomer serine-b-poly(lactide)-b-poly(ethylene glycol)-b-poly(lactide)-b-oligomer serine (OS-PLA-PEG-PLA-OS) pentablock copolymer, as matrix and electrosprayed HGh-loaded chitosan (HGh@CS) nanoparticles (NPs) as principal material. The proton nuclear magnetic resonance spectrum of the pH- and temperature-sensitive OS-PLA-PEG-PLA-OS pentablock copolymer hydrogel proved that this copolymer was successfully synthesized. The HGh was encapsulated in chitosan (CS) NPs by an electrospraying system in acetic acid with appropriate granulation parameters. The scanning electron microscopy images and size distribution showed that the HGh@CS NPs formed had an average diameter of 366.1 ± 214.5 nm with a discrete spherical shape and dispersed morphology. The sol–gel transition of complex gel based on HGh@CS NPs and OS-PLA-PEG-PLA-OS pentablock hydrogel was investigated at 15 °C and pH 7.8 in the sol state and gelled at 37 °C and pH 7.4, which is suitable for the physiological conditions of the human body. The HGh release experiment of the composite system was performed in an in vivo environment, which demonstrated the ability to release HGh, and underwent biodegradation within 32 days. The findings of the investigation revealed that the distribution of HGh@CS NPs into the hydrogel matrix not only improved the mechanical properties of the gel matrix but also controlled the drug release kinetics into the systematic bloodstream, which ultimately promotes the desired therapeutic body growth depending on the distinct concentration used.

Poloxamer 407 modified collagen/β-tricalcium phosphate scaffold for localized delivery of alendronate.

Systemic administration of alendronate is associated with various adverse reactions in clinical settings. To mitigate these side effects, poloxamer 407 (P-407) modified with cellulose was chosen to encapsulate alendronate. This drug-loaded system was then incorporated into a collagen/β-tricalcium phosphate (β-TCP) scaffold to create a localized drug delivery system. Nuclear magnetic resonance spectrum and rheological studies revealed hydrogen bonding between P-407 and cellulose as well as a competitive interaction with water that contributed to the delayed release of alendronate (ALN). Analysis of the degradation kinetics of P-407 and release kinetics of ALN indicated zero-order kinetics for the former and Fickian or quasi-Fickian diffusion for the latter. The addition of cellulose, particularly carboxymethyl cellulose (CMC), inhibited the degradation of P-407 and prolonged the release of ALN. The scaffold's structure increased the contact area of P-407 with the PBS buffer, thereby, influencing the release rate of ALN. Finally, biocompatibility testing demonstrated that the drug delivery system exhibited favorable cytocompatibility and hemocompatibility. Collectively, these findings suggest that the drug delivery system holds promise for implantation and bone healing applications.