Resection and reconstruction of the largest abdominal vein system (the inferior vena cava, hepatic, and portal vein): a narrative review.

Background and Objective: As tumors invade major abdominal veins, surgical procedures are transformed from simple and basic to complicated and challenging. In this narrative review, we focus on what is currently known and not known regarding the technical aspects of major abdominal venous resection and its reconstruction, patency, and oncologic benefit in a cross-cutting perspective. Methods: A systematic literature search was performed in PubMed and Semantic Scholar from inception up to October 18, 2023. We reviewed 106 papers by title, abstract, and full text regarding resection or reconstruction of the inferior vena cava, hepatic vein confluence, portal vein (PV), and middle hepatic vein (MHV) tributaries in living donor liver transplantation (LDLT) in a cross-cutting perspective. Key Content and Findings: The oncologic benefit of aggressive hepatic vein resection with suitable reconstruction against adenocarcinoma remains unclear, and further studies are required to clarify this point. A superior mesenteric/PV resection is now a universal, indispensable, and effective procedure for pancreatic ductal adenocarcinoma. Although many case series using tailor-made autologous venous grafts have been reported, not only size mismatch but also additional surgical incisions and a longer operation time remain obstacles for venous reconstruction. The use of autologous alternative tissue remains only an alternative procedure because the patency rate of customized tubular conduit type to interpose or replace the resected vein is not known. Unlike arterial replacement, venous replacement using synthetic vascular grafts is still rarely reported and there are several inherent limitations except for reconstruction of tributaries of MHV in LDLT. Conclusions: Various approaches to abdominal vein resection and replacement or reconstruction are technically feasible with satisfactory results. Synthetic vascular grafts may be appropriate but have a certain rate of complications.

[Pathogenesis of myelodysplastic syndromes by excessive mitochondrial fragmentation].

Myelodysplastic syndromes (MDS) are a group of heterogenous hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis in which clonal progenitor expansion occurs alongside impaired myelopoiesis. Inflammatory signaling activation due to dysregulated innate immunity is also a hallmark of MDS pathogenesis. We recently established a useful preclinical tool that recapitulates bona fide MDS phenotypes and gene expression profiles based on previously unreported co-mutations discovered during our clinical surveillance of mutations in patients with MDS. Notably, we focused unbiased transcriptome analysis on determining the distinct underlying mediators of MDS etiology, and identified excessive mitochondrial fission-mediated fragmentation in mutant HSCs and progenitors (HSC/Ps). We confirmed excessive mitochondrial fragmentation in HSC/Ps obtained from patients with MDS regardless of the mutational profile. Importantly, in vivo pharmacological inhibition of mitochondrial fission significantly attenuated inflammatory signaling activation, dysplasia formation and ineffective hematopoiesis phenotype, and prolonged survival of MDS mice, suggesting that excessive mitochondrial fragmentation could be a fundamental trigger of MDS pathogenesis. These findings provide new insights into the mechanistic basis of ineffective hematopoiesis, and a clue for targeting bone marrow failure caused by ineffective hematopoiesis in MDS.

Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks.

Scanning electron microscopy (SEM) images are the most widely used tool for evaluating particle morphology; however, quantitative evaluation using SEM images is time-consuming and often neglected. In this study, we aimed to extract features related to particle morphology of pharmaceutical excipients from SEM images using a convolutional neural network (CNN). SEM images of 67 excipients were acquired and used as models. A classification CNN model of the excipients was constructed based on the SEM images. Further, features were extracted from the middle layer of this CNN model, and the data was compressed to two dimensions using uniform manifold approximation and projection. Lastly, hierarchical clustering analysis (HCA) was performed to categorize the excipients into several clusters and identify similarities among the samples. The classification CNN model showed high accuracy, allowing each excipient to be identified with a high degree of accuracy. HCA revealed that the 67 excipients were classified into seven clusters. Additionally, the particle morphologies of excipients belonging to the same cluster were found to be very similar. These results suggest that CNN models are useful tools for extracting information and identifying similarities among the particle morphologies of excipients.

TGA and NMR relaxation measurement of nonmesoporous silica to investigate the amount of hydrolysis product in acetylsalicylic acid adsorbed on silica.

This study investigated a crucial surface property of silica that contributes to the chemical stability of acetylsalicylic acid (ASA) physically adsorbed on silica. Hydrophilic nonmesoporous types of silica were selected, and the number of hydroxyl groups on silica (N(OH)) was evaluated using thermogravimetric analysis (TGA). The ASA-containing silica was stored at 40 °C in drying conditions, and the amount of ASA degradation was quantified based on salicylic acid. From the scatterplots between the number of hydroxyl groups per unit weight (specific surface area (SSA) × N(OH)) and the amount of ASA degradation, it was clarified that in ASA adsorbed on silica, the ASA chemical stability was determined by the formula (the SSA × N(OH)). In addition, a time-domain nuclear magnetic resonance measurement verified the N(OH) result by estimating the interaction between the silica surface and water in an aqueous silica suspension. The N(OH) result was found to be reasonable.

Use of Time-Domain NMR for 1H T1 Relaxation Measurement and Fitting Analysis in Homogeneity Evaluation of Amorphous Solid Dispersion.

This study examined the usefulness of 1H T1 relaxation measurements for evaluating the homogeneity of amorphous solid dispersion (ASD). Indomethacin and polyvinylpyrrolidone were used to prepare two kinds of ASDs. One was inhomogeneous ASD (ASDmelt) prepared by a melt-quenching method, and the other was homogeneous ASD (ASDsolvent) prepared by a solvent evaporation method. The T1 relaxation was measured by the time-domain NMR (TD-NMR) technique using a low-field NMR system. Curve-fitting analysis of T1 relaxation plots was conducted using the Akaike information criterion. This fitting analysis revealed that the T1 relaxation of ASDmelt and ASDsolvent was biphasic and monophasic, respectively. ASDmelt and ASDsolvent were inhomogeneous and homogeneous on a nanometer scale, respectively, considering the spin diffusion of 1H nuclei. These T1 results were consistent with the Raman mapping of ASDs. From the fitting analysis of 1H T1 relaxation, we conclude that TD-NMR is a promising technique for evaluating ASD homogeneity.

Assessment of multiple domains of pain following BNT162b2 mRNA COVID-19 vaccination.

Pain at the injection site is the most frequent reaction among COVID-19 vaccine recipients, but its characteristics were not fully described yet. The purpose of this study was to investigate multiple domains of pain following BNT162b2 mRNA vaccination. We included 107 subjects undergoing primary shot of the vaccination twice into deltoid muscle with a 3-week interval. They completed 6 sessions of pain assessments, one before the first and second dose (1-0, 2-0), and 1st/7th day after the first and second dose (1-1/1-7, 2-1/2-7). Pain visual analog scale (VAS), pain distribution, and pressure pain threshold (PPT) on deltoid muscle were evaluated in each session. The mean VAS (at rest/shoulder motion) was 6.0/27.6 mm at 1-1, and 12.8/34.0 mm at 2-1. Approximately, 90% of recipients showed localized pain within the upper arm. Percentage change of PPTs at 1-1 and 2-1 was bilaterally (ipsilateral/contralateral) decreased to 87.4/89.4% and 80.6/91.0%, which was recovered to the baseline level at 1-7 and 2-7. Temporary, mild-to-moderate intensity, localized distribution, concomitant with bilateral mechanical hyperalgesia on the deltoid muscle, were typical pain characteristics following this vaccination. These findings provide a rationale that will be informative for future recipients. J. Med. Invest. 70 : 355-360, August, 2023.

Continuous Monitoring of the Agglomeration and Sedimentation of Indomethacin Nanosuspensions Using T2 Relaxation Time with Time-Domain NMR.

The time-domain NMR technique was utilized to monitor precisely the physicochemical stability of indomethacin (IMC) nanosuspensions using T2 relaxation time (T2). We investigated whether T2 values can distinguish between agglomeration and sedimentation. Nanosuspensions of IMC were prepared using aqueous wet bead milling with polyvinylpyrrolidone as a stabilizer. Prepared nanosuspensions were divided into two fractions: one was stored in the NMR equipment for continuous T2 measurements and the other was stored in the dispersion analyzer. Measurements of both nanosuspensions were carried out, without dilution, over a period of 24 h at 10-min intervals. Transmission profiles based on multilight scattering technology showed that agglomeration predominantly occurred at 25 and 35 °C immediately after wet bead milling up to 4 h, followed by sedimentation from 4 to 24 h. Upon measuring the T2 relaxation, T2 values at both 25 and 35 °C showed a two-step change-there was a significant prolongation in T2 values immediately after preparation of nanosuspensions up to approx. 4 h and a gradual prolongation in T2 values from approx. 4 to 24 h. Considering the results of transmission profiles, these two-step T2 changes correspond to agglomeration and sedimentation. In other words, this study established that monitoring the T2 values of nanosuspensions could be used to evaluate the agglomeration and sedimentation of contained drug particles. This technique does not directly observe the nanoparticles themselves, but the water molecules. Thus, measurement of T2 relaxation is considered to be a general-purpose technique, independent of the type of drug or polymer.

Micromorphological observation of HLE cells under knockdown of Fascin using LV-SEM.

Liver cancer is one of the most prevalent cancers in Japan with hepatocellular carcinoma (HCC) as the major histological subtype. Successful novel treatments for HCC have been reported; however, recurrences or metastasis may occur, which results in poor prognoses and high mortality of HCC patients. Fascin, an actin-bundling protein, regulates cell adhesion, migration, and invasion. Its overexpression positively correlates with poor prognosis of malignant tumors, and Fascin is considered as one of the tumor biomarkers and therapeutic target proteins. In this study, we attempted to reveal the relationship between Fascin and HCC using HLE, one of the human HCC cell lines. We performed the study with classical immunocytochemistry and recently developed techniques, such as wound-healing assay, spheroid cultivation, and low-vacuum scanning electron microscopy (LV-SEM). Non-Fascin-knockdown (FKD) cell spheroid had a regular spherical appearance with tight cell-cell connections, while FKD cell spheroid had an irregular shape with loose cell-cell connections. Cells of non-FKD spheroid presented fibrous protrusions on the cell surface, contrarily, cells of FKD spheroids showed bulbous-shaped protrusions. Morphological observation of FKD and non-FKD HLE spheroids were performed using LV-SEM. Our study may help to reveal the roles of Fascin in the process of HCC formation and its malignancy.

SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions.

Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers.

Continuous Monitoring of the Hydration Behavior of Hydrophilic Matrix Tablets Using Time-Domain NMR.

Time-domain NMR (TD-NMR) was used for continuous monitoring of the hydration behavior of hydrophilic matrix tablets. The model matrix tablets comprised high molecular weight polyethylene oxide (PEO), hydroxypropyl methylcellulose (HPMC), and polyethylene glycol (PEG). The model tablets were immersed in water. Their T2 relaxation curves were acquired by TD-NMR with solid-echo sequence. A curve-fitting analysis was conducted on the acquired T2 relaxation curves to identify the NMR signals corresponding to the nongelated core remaining in the samples. The amount of nongelated core was estimated from the NMR signal intensity. The estimated values were consistent with the experiment measurement values. Next, the model tablets immersed in water were monitored continuously using TD-NMR. The difference in hydration behaviors of the HPMC and PEO matrix tablets was then characterized fully. The nongelated core of the HPMC matrix tablets disappeared more slowly than that of the PEO matrix tablets. The behavior of HPMC was significantly affected by the PEG content in the tablets. It is suggested that the TD-NMR method has potential to be utilized to evaluate the gel layer properties, upon replacement of the immersion medium: purified (nondeuterated) water is replaced with heavy (deuterated) water. Finally, drug-containing matrix tablets were tested. Diltiazem hydrochloride (a highly water-soluble drug) was employed for this experiment. Reasonable in vitro drug dissolution profiles, which were in accordance with the results from TD-NMR experiments, were observed. We concluded that TD-NMR is a powerful tool to evaluate the hydration properties of hydrophilic matrix tablets.

Downregulation of fascin induces collective cell migration in triple­negative breast cancer.

Breast cancer (BC) is one of the most common types of cancer affecting female patients. Triple­negative BC (TNBC) is an aggressive subtype. Fascin, an actin­bundling protein, serves a significant role in cancer metastasis. Fascin overexpression is associated with poor prognosis of BC. To confirm the relationship between fascin expression and BC malignancy, the present study reviewed clinical data from 100 Japanese patients with BC and performed fresh immunohistochemical fascin examination of tissue samples. Statistical analyses showed metastasis or recurrence in 11 of 100 patients and a significant association between high fascin expression and poor prognosis. The TNBC subtype was also associated with high fascin expression. However, a few cases developed poor prognosis regardless of negative or slightly positive fascin expression. The present study established fascin knockdown (FKD) MDA­MB­231, a TNBC cell line, and investigated morphological effects of fascin on TNBC cells. FKD cells exhibited cell­cell connections and bulbous nodules of various sizes on the cell surface. Conversely, non­FKD MDA­MB­231 cells exhibited loose cell­cell connections with numerous filopodia on the cell surface. Filopodia, actin­rich plasma membrane protrusions, are composed of fascin and control cell­cell interaction, migration and wound healing. Cancer metastasis is conventionally classified into two mechanisms: single and collective cell migration. Fascin increases cancer metastasis by single cell migration via filopodia on the cell surface. However, the present study suggested that following FKD, TNBC cells lost filopodia and exhibited collective cell migration.

A Data-Driven Approach to Predicting Tablet Properties after Accelerated Test Using Raw Material Property Database and Machine Learning.

The purpose of this study was to develop a model for predicting tablet properties after an accelerated test and to determine whether molecular descriptors affect tablet properties. Tablets were prepared using 81 types of active pharmaceutical ingredients, with the same formulation and three different levels of compression pressure. The tablet properties measured were the tensile strength and disintegration time of tablets after two weeks of accelerated test. The material properties measured were the change in tablet thickness before and after the accelerated test, maximum swelling force, swelling time, and swelling rate. The acquired data were added to our previously constructed database containing a total of 20 material properties and 3381 molecular descriptors. The feature importance values of molecular descriptors, material properties and the compression pressure for each tablet property were calculated by random forest, which is one type of machine learning (ML) that uses ensemble learning and decision trees. The results showed that more than half of the top 25 most important features were molecular descriptors for both tablet properties, indicating that molecular descriptors are strongly related to tablet properties. A prediction model of tablet properties was constructed by eight ML types using 25 of the most important features. The results showed that the boosted neural network exhibited the best prediction accuracy and was able to predict tablet properties with high accuracy. A data-driven approach is useful for discovering intricate relationships hidden within complex and large data sets and predicting tablet properties after an accelerated test.

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales.

The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R2= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.

Efficient Transformation of Polymer Main Chain Catalyzed by Macrocycle Metal Complexes via Pseudorotaxane Intermediate.

Post-synthesis modification of polymers streamlines the synthesis of functionalized polymers, but is often incomplete due to the negative polymer effects. Developing efficient polymer reactions in artificial systems thus represents a long-standing objective in the fields of polymer and material science. Here, we show unprecedented macrocycle-metal-complex-catalyzed systems for efficient polymer reaction that result in 100 % transformation of the main chain functional groups presumably via a processive mode reaction. The complete polymer reactions were confirmed in not only intramolecular reaction (hydroamination) but also intermolecular reaction (hydrosilylation) by using Pd- and Pt-macrocycle-catalyzed systems. The most fascinating feature of the both reactions is that higher-molecular-weight polymers reach completion faster. Various studies suggested that the reactions occur in the catalyst cavity via the formation of a supramolecular complex between the macrocycle catalyst and polymer substrate like pseudorotaxane, which should be of characteristic of the efficient polymer reactions progressing in a processive mode.

Mitochondrial dynamics as a pathobiological mediator of clonal myeloid disorders.

Myeloid malignancies, including myelodysplastic syndromes and acute myeloid leukemia, are a group of clonal hematopoietic stem cell (HSC) diseases. The incidence increases with global population aging. Genome sequencing uncovered mutational profiles in patients with myeloid malignancies and healthy elderly individuals. However, the molecular and cellular basis of disease development remains unclear. Accumulating evidence shows mitochondrial involvement in the pathogenesis of myeloid malignancies, aging-related HSC phenotypes, and clonal hematopoiesis. Mitochondria are dynamic organelles that continuously undergo fission and fusion processes to maintain their function, integrity, and activity. Mitochondria could be a hub of various biological processes that underlie cellular and systemic homeostasis. Thus, mitochondrial dysfunction could directly lead to the disruption of cellular homeostasis and the development of various disorders, including cancer. Notably, emerging data have revealed that mitochondria dynamics also primarily affect not only mitochondrial function and activity but also cellular homeostasis, the aging process, and tumorigenesis. Here, by focusing on mitochondrial dynamics, we highlight the current understanding of mitochondrial roles as a pathobiological mediator of myeloid malignancies and aging-related clonal hematopoiesis.

The early neutrophil-committed progenitors aberrantly differentiate into immunoregulatory monocytes during emergency myelopoiesis.

Inflammatory stimuli cause a state of emergency myelopoiesis leading to neutrophil-like monocyte expansion. However, their function, the committed precursors, or growth factors remain elusive. In this study we find that Ym1+Ly6Chi monocytes, an immunoregulatory entity of neutrophil-like monocytes, arise from progenitors of neutrophil 1 (proNeu1). Granulocyte-colony stimulating factor (G-CSF) favors the production of neutrophil-like monocytes through previously unknown CD81+CX3CR1lo monocyte precursors. GFI1 promotes the differentiation of proNeu2 from proNeu1 at the cost of producing neutrophil-like monocytes. The human counterpart of neutrophil-like monocytes that also expands in response to G-CSF is found in CD14+CD16- monocyte fraction. The human neutrophil-like monocytes are discriminated from CD14+CD16- classical monocytes by CXCR1 expression and the capacity to suppress T cell proliferation. Collectively, our findings suggest that the aberrant expansion of neutrophil-like monocytes under inflammatory conditions is a process conserved between mouse and human, which may be beneficial for the resolution of inflammation.

A specific G9a inhibitor unveils BGLT3 lncRNA as a universal mediator of chemically induced fetal globin gene expression.

Sickle cell disease (SCD) is a heritable disorder caused by ß-globin gene mutations. Induction of fetal γ-globin is an established therapeutic strategy. Recently, epigenetic modulators, including G9a inhibitors, have been proposed as therapeutic agents. However, the molecular mechanisms whereby these small molecules reactivate γ-globin remain unclear. Here we report the development of a highly selective and non-genotoxic G9a inhibitor, RK-701. RK-701 treatment induces fetal globin expression both in human erythroid cells and in mice. Using RK-701, we find that BGLT3 long non-coding RNA plays an essential role in γ-globin induction. RK-701 selectively upregulates BGLT3 by inhibiting the recruitment of two major γ-globin repressors in complex with G9a onto the BGLT3 gene locus through CHD4, a component of the NuRD complex. Remarkably, BGLT3 is indispensable for γ-globin induction by not only RK-701 but also hydroxyurea and other inducers. The universal role of BGLT3 in γ-globin induction suggests its importance in SCD treatment.

Usefulness of Applying Partial Least Squares Regression to T2 Relaxation Curves for Predicting the Solid form Content in Binary Physical Mixtures.

This study applied partial least squares (PLS) regression to nuclear magnetic resonance (NMR) relaxation curves to quantify the free base of an active pharmaceutical ingredient powder. We measured the T2 relaxation of intact and moisture-absorbed physical mixtures of tetracaine free base (TC) and its hydrochloride salt (TC·HCl). The obtained T2 relaxation curves were analyzed by two methods, one using a previously reported T2 relaxation time (T2), and the other using PLS regression. The accuracy of estimating TC was inadequate when using previous T2 values because the moisture-absorbed physical mixtures showed biphasic T2 relaxation curves. By contrast, the entire measured whole of the T2 relaxation curves was used as input variables and analyzed by PLS regression to quantify the content of TC in the moisture-absorbed TC/TC·HCl. Based on scatterplots of theoretical versus predicted TC, the obtained PLS model exhibited acceptable coefficients of determination and relatively low root mean squared error values for calibration and validation data. The statistical values confirmed that an accurate and reliable PLS model was created to quantify TC in even moisture-absorbed TC/TC·HCl. The bench-top low-field NMR instrument used to apply PLS regression to the T2 relaxation curve may be a promising tool in process analytical technology.

Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning.

Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development.

Low-Field NMR to Characterize the Crystalline State of Ibuprofen Confined in Ordered or Nonordered Mesoporous Silica.

The crystalline state of ibuprofen (IBU) confined in mesoporous silica was characterized using low-field time-domain nuclear magnetic resonance (TD-NMR). IBU was loaded into ordered (Santa Barbara Amorphous-15 [SBA-15]; SBA) or nonordered mesoporous silica (Sylysia 320; SYL) using a well-known incipient wetness impregnation method. The dissolution profile of IBU from the silica was measured. The IBU-loaded SBA showed a relatively higher drug concentration at 10 and 20 min, which was typical of a supersaturated solution. However, it did not maintain that concentration. By contrast, the IBU-loaded SYL did not show such a dissolution profile in the early stage. To characterize the crystalline state of IBU confined in silica, the T1 relaxation time of IBU-loaded silica powder was measured and analyzed by curve fitting. Monophasic T1 relaxation was observed for IBU-loaded SBA. This may indicate that the amorphous phase, which has various molecular mobilities, was close to within the length of 1H spin diffusion. The TD-NMR technique, even if the sample is powder, can rapidly and easily measure NMR relaxation. Therefore, it can be useful toward fully characterizing the crystalline state of drugs confined in mesopores.