Specific Fragments Research Articles

A static quantum embedding scheme based on coupled cluster theory.

We develop a static quantum embedding scheme that utilizes different levels of approximations to coupled cluster (CC) theory for an active fragment region and its environment. To reduce the computational cost, we solve the local fragment problem using a high-level CC method and address the environment problem with a lower-level Møller-Plesset (MP) perturbative method. This embedding approach inherits many conceptual developments from the hybrid second-order Møller-Plesset (MP2) and CC works by Nooijen [J. Chem. Phys. 111, 10815 (1999)] and Bochevarov and Sherrill [J. Chem. Phys. 122, 234110 (2005)]. We go beyond those works here by primarily targeting a specific localized fragment of a molecule and also introducing an alternative mechanism to relax the environment within this framework. We will call this approach MP-CC. We demonstrate the effectiveness of MP-CC on several potential energy curves and a set of thermochemical reaction energies, using CC with singles and doubles as the fragment solver, and MP2-like treatments of the environment. The results are substantially improved by the inclusion of orbital relaxation in the environment. Using localized bonds as the active fragment, we also report results for N=N bond breaking in azomethane and for the central C-C bond torsion in butadiene. We find that when the fragment Hilbert space size remains fixed (e.g., when determined by an intrinsic atomic orbital approach), the method achieves comparable accuracy with both a small and a large basis set. Additionally, our results indicate that increasing the fragment Hilbert space size systematically enhances the accuracy of observables, approaching the precision of the full CC solver.

Fragment of Interest: Personalized Video Fragment Recommendation with Inter-Fragment & Intra-Fragment Contextual Effect

In today’s fast-paced digital landscape, the attention span of users consuming video content is alarmingly brief, often as short as 15 seconds for music or entertainment videos and 6 minutes for lecture videos. This presents a significant challenge for video producers and platform providers as they seek to engage users with longer content. One promising solution involves recommending specific fragments within longer videos that align with individual user profiles. In this paper, we address this challenge by introducing a novel framework for video fragment recommendations, guided by three key insights. First, we implement a Self-Attention Block that captures the inter-fragment contextual effect, enhancing the relevance of recommendations. Second, we incorporate video-level preferences to ensure that the fragment recommendations are consistent with users’ overall interests. Third, we propose a Self-Attentive Herding Effect (SAHE) module to model the intra-fragment contextual effect, specifically the herding effect of time-sync comments within a fragment. To evaluate the effectiveness of our proposed method, we conduct extensive experiments comparing our model against the state-of-the-art approaches in terms of NDCG@K and Recall@K. Our results demonstrate that the model effectively leverages inter-fragment, intra-fragment contextual effects, and video-level preferences, outperforming existing methods. Additionally, we carry out empirical experiments to analyze the key components and parameters of the proposed model, providing further insights into its performance.

Stature estimation equations from fragmentary long bones based on a modern Eastern Mediterranean assemblage.

Stature estimation is central in forensic anthropology and very important in bioarchaeology. For this reason, several different methods have been proposed, employing different skeletal elements and statistical approaches. A major issue with skeletonized individuals is that their bones are often found fragmented, a taphonomic parameter that limits the application of many available methods. As a result, attempts have been made to create equations to predict either directly stature or long bone length (which can then be used with current stature prediction equations) from bone fragments. The current paper is a contribution in this direction. The femur, tibia and humerus of 76 individuals from a modern Greek skeletal collection were divided into different segments using a landmark approach. Subsequently, univariate and multivariate equations were created to predict both maximum long bone length and stature from the "bone fragments". The models varied in performance depending on the specific bone fragment used, the number of variables simultaneously employed for prediction and the sex of the individuals. Although the models used to directly predict stature from bone fragment dimensions should be treated cautiously because the stature of the assemblage from the Athens Collection had itself been anatomically estimated, the results are valuable towards highlighting the complex association between bone dimensions, long bone length and living/estimated stature.

Clinical Manifestations and Molecular Identification of Giardia duodenalis in Pediatric and Adolescent Cancer Patients in Southwestern Iran.

This study aimed to investigate the clinical and molecular characteristics of Giardia duodenalis (G. duodenalis) infection and identify potential risk factors in children and teenagers with malignancies in Shiraz, southwestern Iran. A total of 200 fresh fecal samples were collected from children and adolescents suffering from 32 different cancer types at Amir, Nemazee, and Saadi hospitals affiliated with Shiraz University of Medical Sciences between October 2021 and May 2022. Direct microscopy using saline and iodine wet mount was conducted, and all fecal samples were rechecked by SSU-PCR. Subsequently, a specific fragment of the tpi gene was amplified on all samples for prevalence, sequencing, and assemblage identification. Our study found a 4% (8/200) prevalence of G. duodenalis using microscopy and PCR. The molecular findings were consistent with the microscopic results. All eight positive samples with SSU-rRNA gene were also detected as positive with tpi gene and were correctly sequenced. Among the examined cancer patients, two assemblages were identified: A [sub-assemblage AI (2/8, 25%) and sub-assemblage AII (3/8, 37.5%)] and B [sub-assemblage BIV (3/8, 37.5%)]. Notably, patients were more vulnerable to G. duodenalis infection after receiving at least 8 treatment episodes (p < 0.05) and displaying gastrointestinal symptoms (p > 0.05). The demographic characteristics of cancer patients with G. duodenalis infection and the statistical conclusions were separately detailed. The small sample size and low prevalence rate in this study hindered precise epidemiological conclusions. Nonetheless, the results suggest that G. duodenalis infection among cancer patients in Shiraz city originates from humans, without any specific animal groups (C-H) involved.

Transfer RNAs are Linked to Ischemic Stroke and Major Bleeding in Patients with End-Stage Kidney Disease.

Patients with end-stage kidney disease (ESKD) are at very high risk for thromboembolism and bleeding. This study aimed to identify small noncoding RNAs (sncRNAs), specifically microRNAs and transfer-RNA (tRNA)-derived fragments (tRFs), as potential novel biomarkers for predicting thromboembolism and bleeding in this high-risk population. In this sncRNA discovery research, we leveraged the VIVALDI cohort, consisting of 625 ESKD patients on hemodialysis, to conduct two nested case-control studies, each comprising 18 participants. The primary outcomes were ischemic stroke in the first study and major bleeding in the second. Plasma samples were processed using the miND pipeline for RNA-seq analysis to investigate differential expression of microRNAs and tRNA/tRFs between cases and their respective matched controls, with results stringently adjusted for the false discovery rate (FDR). No significant differential expression of microRNAs for either ischemic stroke or major bleeding outcomes was observed in the two nested case-control studies. However, we identified four tRNAs significantly differentially expressed in ischemic stroke cases and seven in major bleeding cases, compared with controls (FDR < 0.1). Coverage plots indicated that specific tRNA fragments (tRFs), rather than full-length tRNAs, were detected, however. Alternative mapping approaches revealed challenges and technical limitations that precluded in-depth differential expression analyses on these specific tRFs. Yet, they also underscored the potential of tRNAs and tRFs as markers for thromboembolism and bleeding. While microRNAs did not show significant differential expression, our study identifies specific tRNAs/tRFs as potential novel biomarkers for ischemic stroke and major bleeding in ESKD patients.

Design of a portable bioassay system based on highly efficent nanozyme and Au-DNA nanocluster: Toward current/colorimetric dual-signal detection of sugarcane smut

Sugarcane smut is a major agricultural pest affecting sugarcane yield and quality, posing a significant threat to the sustainable and healthy development of the global sugar industry. The best strategy for controlling Sugarcane smut is early and precise diagnosis. An intelligent mobile bio-platform is constructed based on Au-Co/Zn ZIF nanozymes and Au-DNA nanoclusters for current/colorimetric dual signal detection of the sugarcane smut pathogen. Efficient and specific cycling of the specific gene fragment (bE4′) for Sugarcane smut is achieved by strand displacement reaction. The output strand then forms Au-DNA nanoclusters with AuNPs-DNA, and the bioconjugate is captured by H2 probe, which results in the formation of more double-stranded structures, causing methylene blue (MB) to adsorb on electrode. The anodic nanozyme efficiently catalyzes the oxidation of glucose, generating a large number of electrons, and MB in the system is therefore reduced to MBH, causing a color change. This results in distinct current/colorimetric dual output signals, significantly enhancing the accuracy. A capacitor is further coupled with the system to improve the sensitivity. The detection signals are read in real-time by a smartphone. The developed method for sugarcane smut genes shows good linearity within a concentration range of 0.0001–10000 pM, with a detection limit as low as 23.59 aM (S/N = 3). This work integrates advanced technologies from various fields to create a portable sensing device with extremely high sensitivity and precision for detecting sugarcane smut. It provides a reliable new option for early and precise diagnosis of the disease and offers valuable theoretical and technical insights for early prevention, intelligent detection, and real-time monitoring of other plant diseases.

Predicting Collision-Induced-Dissociation Tandem Mass Spectra (CID-MS/MS) Using Ab Initio Molecular Dynamics.

Compound identification is at the center of metabolomics, usually by comparing experimental mass spectra against library spectra. However, most compounds are not commercially available to generate library spectra. Hence, for such compounds, MS/MS spectra need to be predicted. Machine learning and heuristic models have largely failed except for lipids. Here, quantum chemistry software can be used to predict mass spectra. However, quantum chemistry predictions for collision induced dissociation (CID) mass spectra in LC-MS/MS are rare. We present the CIDMD (Collision-Induced Dissociation via Molecular Dynamics) framework to model CID-based MS/MS spectra. It uses first-principles molecular dynamics (MD) to simulate the physical process of molecular collisions in CID tandem mass spectrometry. First, molecular ions are constructed at specific protonation sites. Using density functional theory, these protonated ions are targeted by argon collider gas atoms at user-specified velocities. Subsequent bond breakages are simulated over time for at least 1,000 fs. Each simulation is repeated multiple times from various collisional directions. Fragmentations are accumulated over those repeated collisions to generate CIDMD in silico mass spectra. Twelve small metabolites (<205 Da) were selected to test the accuracy of this framework in comparison to experimental MS/MS spectra. When testing different protomers, collider velocities, number of simulations, simulation time and impact factor b cutoffs, we yielded 261 predicted mass spectra. These in silico spectra resulted in entropy similarity scores of an average 624 ± 189 for all 261 spectra compared to their corresponding experimental spectra, which improved to 828 ± 77 when using optimal parameters of the most probable protomers for 12 molecules. With increasing molecular mass, higher velocities achieved better results. Similarly, different protomers showed large differences in fragmentation; hence, with increasing numbers of protomers and tautomers, the average CIDMD prediction accuracy decreased. Mechanistic details showed that specific fragment ions can be produced from different protomers via multiple fragmentation pathways. We propose that CIDMD is a suitable tool to predict mass spectra of small metabolites like produced by the gut microbiome.

A Multiplex PCR Assay for Simultaneous Detection of Giardia duodenalis, Cryptosporidium parvum, Blastocystis spp. and Enterocytozoon bieneusi in Goats.

Giardia duodenalis, Cryptosporidium parvum, Blastocystis spp. and Enterocytozoon bieneusi are four common zoonotic parasites associated with severe diarrhea and enteric diseases. In this study, we developed a multiplex PCR assay for the simultaneous detection of these four zoonotic protozoans in goat stool samples and assessed its detection efficiency. Specific primers were designed from conserved gene sequences retrieved from GenBank, and the PCR conditions were optimized. Genomic DNA from 130 samples was subjected to both single-target PCR and multiplex PCR. The multiplex PCR assay successfully amplified specific gene fragments (G. duodenalis, 1400 bp; C. parvum, 755 bp; Blastocystis spp., 573 bp; E. bieneusi, 314 bp). The assay sensitivity was ≥102 copies of pathogenic DNA clones with high specificity confirmed by negative results for other intestinal parasites. The detection rates were 23.08% (30/130) for G. duodenalis, 24.62% (32/130) for C. parvum, 41.54% (54/130) for Blastocystis spp., and 12.31% (16/130) for E. bieneusi, matching the single-target PCR results. The sensitivity and predictive values were 100.00%. This multiplex PCR provided a rapid, sensitive, specific, and cost-effective approach for detecting these four parasites. It also provided essential technical support for the rapid detection and epidemiological investigation of G. duodenalis, C. parvum, Blastocystis spp., and E. bieneusi infections in goat fecal samples.

Kohn-Sham fragment energy decomposition analysis.

We introduce the concept of Kohn-Sham fragment localized molecular orbitals (KS-FLMOs), which are Kohn-Sham molecular orbitals (MOs) localized in specific fragments constituting a generic molecular system. In detail, we minimize the local electronic energies of various fragments, while maximizing the repulsion between them, resulting in the effective localization of the MOs. We use the developed KS-FLMOs to propose a novel energy decomposition analysis, which we name Kohn-Sham fragment energy decomposition analysis, which allows for rationalizing the main non-covalent interactions occurring in interacting systems both in vacuo and in solution, providing physical insights into non-covalent interactions. The method is validated against state-of-the-art energy decomposition analysis techniques and with high-level calculations.

PDCD4 interacting with PIK3CB and CTSZ promotes the apoptosis of multiple myeloma cells.

The role of programmed cell death 4 (PDCD4) in multiple myeloma (MM) development remains unknown. Here, we investigated its role and action mechanism in MM. Bioinformatic analysis indicated that patients with MM and high PDCD4 expression had higher overall survival than those with low PDCD4 expression. PDCD4 expression promoted MM cell apoptosis and inhibited their viability invitro and tumor growth invivo. RNA-binding protein immunoprecipitation sequencing analysis showed that PDCD4 is bound to the 5' UTR of the apoptosis-related genes PIK3CB, Cathepsin Z (CTSZ), and X-chromosome-linked apoptosis inhibitor (XIAP). PDCD4 knockdown reduced the cell apoptosis rate, which was rescued by adding PIK3CB, CTSZ, or XIAP inhibitors. Dual luciferase reporter assays confirmed the internal ribosome entry site (IRES) activity of the 5' UTRs of PIK3CB and CTSZ. An RNA pull-down assay confirmed binding of the 5' UTR of PIK3CB and CTSZ to PDCD4, identifying the specific binding fragments. PDCD4 is expected to promote MM cell apoptosis by binding to the IRES domain in the 5' UTR of PIK3CB and CTSZ and inhibiting their translation. Our findings suggest that PDCD4 plays an important role in MM development by regulating the expression of PIK3CB, CTSZ, and XIAP, and highlight new potential molecular targets for MM treatment.

Correlations of PSGL-1 VNTR polymorphism with the susceptibility to severe HFMD associated with EV-71 and the immune status after infection

Enterovirus 71 (EV-71) has strong neurotropism, and it is the main pathogen causing severe hand, foot, and mouth disease (HFMD). In clinical observations, significant differences were observed in the severity and prognosis of HFMD among children who were also infected with EV-71. Genetic differences among individuals could be one of the important causes of differences in susceptibility to EV-71–induced HFMD. As P-selectin glycoprotein ligand-1 (PSGL-1) is an important receptor of EV-71, the correlation between single-nucleotide polymorphisms (SNPs) in PSGL-1 and the susceptibility to severe HFMD following EV-71 infection is worth studying. Given the role of PSGL-1 in immunity, the correlations between PSGL-1 SNPs and the immune status after EV-71 infection are also worth studying. Meanwhile, PSGL-1 variable number of tandem repeats (VNTR) represents a research hotspot in cardiovascular and cerebrovascular diseases, but PSGL-1 VNTR polymorphism has not been investigated in HFMD caused by EV-71 infection. In this study, specific gene fragments were amplified by polymerase chain reaction, and PSGL-1 VNTR sequences were genotyped using an automatic nucleic acid analyzer. The correlations of PSGL-1 VNTR polymorphism with the susceptibility to EV-71–associated severe HFMD and the post-infection immune status were analyzed. The PSGL-1 VNTR A allele was identified as a susceptible SNP for severe HFMD. The risk of severe HFMD was higher for AA + AB genotype carriers than for BB genotype carriers. The counts of peripheral blood lymphocyte subsets were lower in AA + AB genotype carries than in BB genotype carries. In conclusion, PSGL-1 VNTR polymorphism is associated with the susceptibility to EV-71–induced severe HFMD and the immune status after infection. PSGL-1 VNTR might play a certain role in the pathogenesis of severe cases.

Using machine learning techniques for exploration and classification of laboratory data

Abstract Objectives The study aims to acquaint readers with six widely used machine learning (ML) techniques (Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), k-means, hierarchical clustering and the decision tree models (rpart and random forest)) that might be useful for the analysis of laboratory data. Methods Utilizing a recently validated data set from lung cancer diagnostics, we investigate how ML can support the search for a suitable tumor marker panel for the differentiation of small cell (SCLC) and non-small cell lung cancer (NSCLC). Results The ML techniques used here effectively helped to gain a quick overview of the data structures and provide initial answers to the clinical questions. Dimensionality reduction techniques such as PCA and UMAP offered insightful visualization and impression of the data structure, suggesting the existence of two tumor groups with a large overlap of largely inconspicuous values. This impression was confirmed by a cluster analysis with the k-means algorithm, indicative of unsupervised learning. For supervised learning, decision tree models like rpart or random forest demonstrated their utility in differential diagnosis of the two tumor types. The rpart model, which constructs binary decision trees based on the recursive partitioning algorithm, suggests a tree involving four serum tumor markers (STMs), which were confirmed by the random forest approach. Both highlighted pro-gastrin-releasing peptide (ProGRP), neuron specific enolase (NSE), cytokeratin-19 fragment (CYFRA 21-1) and cancer antigen (CA) 72-4 as key tumor markers, aligning with the outcomes of the initial statistical analysis. Cross-validation of the two proposals showed a higher area under the receiver operating characteristic (AUROC) curve of 0.95 with a 95 % confidence interval (CI) of 0.92–0.97 for the random forest model compared to an AUROC curve of 0.88 (95 % CI: 0.83–0.93). Conclusions ML can provide a useful overview of inherent medical data structures and distinguish significant from less pertinent features. While by no means replacing human medical and statistical expertise, ML can significantly accelerate the evaluation of medical data, supporting a more informed diagnostic dialogue between physicians and statisticians.

Assessment of the usefulness of β-cyclodextrin in the removal of progesterone from the environment

The paper presents the results and interpretation of theoretical calculations for the progesterone–β-cyclodextrin (P@β-CD; G4MP2) and progesterone–β-cyclodextrin–β-cyclodextrin (P@β-CD–β-CD; G2) systems. The geometry of the progesterone molecule was optimized on basis of the DFT theory using the B3LYP, PBE1PBE and M06-2X functionals, for selected Pople basis sets [6-31G, 6‐31++G, 6‐31++G(d,p), 6-311G, 6‐311++G, 6‐311++G(d,p)] and the Dunning basis set (aug-cc-pVDZ). Presented results of theoretical calculations provide insight into the mechanism of formation of supramolecular systems of progesterone and β-cyclodextrin, allowing us to understand the impact of differences in the polarizability of specific fragments of the “guest” molecule, through the analysis of the Mulliken population distribution, on the tendency for equilibrium inclusion by “host” molecules characterized by selective affinity towards hydrophilic and hydrophobic molecular systems. The comparison of model structures of “guest–host” systems with 1:1 and 1:2 stoichiometry allows us to assess the contribution of a given type of non-covalent interactions (hydrogen bonds, van der Waals interactions, London dispersion forces) in the formation of supramolecular complexes. Due to the complexity of the real research object, including interactions between large molecules in solution, in this work it was decided to choose an approximation that reflects only the key effects. The results presented in this paper constitute a starting point for both theoretical research on analogous supramolecular systems based on β-cyclodextrin derivatives, as well as instrumental studies (NMR, FT–IR, HPLC) of preparations of real inclusion complexes. The approach adopted now can contribute to a better understanding of the phenomenon used in the development of water purification technologies.

Multiplex polymerase chain reaction (mPCR) assay for detection and differentiation of Campylobacter jejuni and Campylobacter coli in carcasses of chicken

Introduction: Campylobacter is a major cause of gastroenteritis in developed countries. In Brazil, the incidence of this disease and consequently those that may occur after the infection is unknown. The prevalence of Campylobacter in raw poultry products varies widely. Although Brazil is the largest world exporter of chicken there is little information on the contamination of this product by Campylobacter. Objective: develop a multiplex PCR for the detection and differentiation between C. jejuni and C coli. Methods: The PCR was developed using mapA primers specific for detection of C. jejuni and ceuE primers specific for detection of C. coli which produce specific DNA fragments of 202-bp and 889-pb, respectively. The standardized PCR was tested on 11 different isolates of Campylobacter and on 22 non-Campylobacter species and the specificity was 100%. Results: Campylobacter was detected from chicken skin artificially contaminated with approximately 50 colony forming unit (CFU) of Campylobacter per 10-g, after 48 h of selective enrichment. Campylobacter spp. was detected in thirteen (46.4%) of 28 analyzed chicken carcasses purchased from local retail market. Specific DNA fragments of the PCR confirmed the presence of C. jejuni and C. coli. Conclusion: The standardized multiplex PCR was found to be sensitive and specific on the detection and simultaneously differentiation of C. jejuni and C. coli after 48h of analysis.

Quantitative Prediction of Human Immunodeficiency Virus Drug Resistance.

Drug resistance of pathogens, including viruses, is one of the reasons for decreased efficacy of therapy. Considering the impact of HIV type 1 (HIV-1) on the development of progressive immune dysfunction and the rapid development of drug resistance, the analysis of HIV-1 resistance is of high significance. Currently, a substantial amount of data has been accumulated on HIV-1 drug resistance that can be used to build both qualitative and quantitative models of HIV-1 drug resistance. Quantitative models of drug resistance can enrich the information about the efficacy of a particular drug in the scheme of antiretroviral therapy. In our study, we investigated the possibility of developing models for quantitative prediction of HIV-1 resistance to eight protease inhibitors based on the analysis of amino acid sequences of HIV-1 protease for 900 virus variants. We developed random forest regression (RFR), support vector regression (SVR), and self-consistent regression (SCR) models using binary vectors containing values from 0 or 1, depending on the presence of a specific peptide fragment in each amino acid sequence as independent variables, while fold ratio, reflecting the level of resistance, was the predicted variable. The SVR and SCR models showed the highest predictive performances. The models built demonstrate reasonable performances for eight out of nine (R2 varied from 0.828 to 0.909) protease inhibitors, while R2 for predicting tipranavir fold ratio was lower (R2 was 0.642). We believe that the developed approach can be applied to evaluate drug resistance of molecular targets of other viruses where appropriate experimental data are available.

Thermal desorption – gas chromatography – mass spectrometry (TD-GC-MS) analysis of PFAS used in food contact materials

Per- and polyfluoroalkyl substances (PFAS) are used in food contact materials (FCMs), e.g. as production aids in the fabrication of PTFE based coatings for kitchenware or as additives in paper and board. Growing concerns about the environment and health related to PFAS have led to an increasing interest in monitoring PFAS levels in FCMs as well as their migration into food. In this study, method development for the analysis of PFAS by thermal desorption – gas chromatography – mass spectrometry (TD-GC-MS) was done. In addition to fluorotelomer alcohols (FTOHs), which are the only PFAS commonly analysed by GC-MS, it was proven that perfluorocarboxylic acids (PFCAs) and per- and polyfluoroether carboxylic acids (PFECAs) as well as their thermolysis products, perfluoroethers (PFEs) and perfluoroalkenes, can be analysed by GC-MS without prior derivatization. Screening for PFCAs and FTOHs was possible by electron impact ionization (EI) using group specific SIM fragments. Confirmation of identity has been done by EI scans as well as chemical ionization (CI) SIM measurements. LODs (limits of detection) of PFCAs, FTOHs and PFECAs in the TD-GC-MS instrument were in the low pg range. Thermal degradation of PFCAs and PFECAs during TD-GC-MS measurement was investigated.

HCR-Assisted RTF-EXPAR-Based Lateral Flow Analysis for Sensitive Detection of H1N1 Influenza Virus.

The H1N1 influenza virus is a significant pathogen responsible for seasonal influenza, and its frequent outbreaks pose substantial challenges to global public health. The present study successfully developed a lateral flow analysis platform that integrates reverse transcription-free exponential amplification reaction (RTF-EXPAR) and hybridization chain reaction (HCR) processes with functionalized quantum dots for the direct detection of H1N1 influenza virus RNA, eliminating the need for reverse transcription. The fluorescence signal on the band recorded with a smartphone can be utilized for the quantitative determination of the target. Interestingly, the dual signal amplification strategy exhibits high sensitivity with a remarkably low detection limit of 10 aM. Moreover, this platform exhibits excellent flexibility and universality, where the various pathogens can be determined by replacing the specific nucleic acid fragments in RTF-EXPAR. The aforementioned advantages reveal its huge potential in the early diagnosis of H1N1 influenza virus infection and developing point-of-care testing (POCT) equipment for nucleic acid analysis.

TRNA-derived fragments in T lymphocyte-beta cell crosstalk and in type 1 diabetes pathogenesis in NOD mice.

tRNAs play a central role in protein synthesis. Besides this canonical function, they were recently found to generate non-coding RNA fragments (tRFs) regulating different cellular activities. The aim of this study was to assess the involvement of tRFs in the crosstalk between immune cells and beta cells and to investigate their contribution to the development of type 1 diabetes. Global profiling of the tRFs present in pancreatic islets of 4- and 8-week-old NOD mice and in extracellular vesicles released by activated CD4+ T lymphocytes was performed by small RNA-seq. Changes in the level of specific fragments were confirmed by quantitative PCR. The transfer of tRFs from immune cells to beta cells occurring during insulitis was assessed using an RNA-tagging approach. The functional role of tRFs increasing in beta cells during the initial phases of type 1 diabetes was determined by overexpressing them in dissociated islet cells and by determining the impact on gene expression and beta cell apoptosis. We found that the tRF pool was altered in the islets of NOD mice during the initial phases of type 1 diabetes. Part of these changes were triggered by prolonged exposure of beta cells to proinflammatory cytokines (IL-1β, TNF-α and IFN-γ) while others resulted from the delivery of tRFs produced by CD4+ T lymphocytes infiltrating the islets. Indeed, we identified several tRFs that were enriched in extracellular vesicles from CD4+/CD25- T cells and were transferred to beta cells upon adoptive transfer of these immune cells in NOD.SCID mice. The tRFs delivered to beta cells during the autoimmune reaction triggered gene expression changes that affected the immune regulatory capacity of insulin-secreting cells and rendered the cells more prone to apoptosis. Our data point to tRFs as novel players in the crosstalk between the immune system and insulin-secreting cells and suggest a potential involvement of this novel class of non-coding RNAs in type 1 diabetes pathogenesis. Sequences are available from the Gene Expression Omnibus (GEO) with accession numbers GSE242568 and GSE256343.

Development of UPLC–MS/MS method for the simultaneous quantification of valproic acid and phenytoin in human plasma and application to study pharmacokinetic interaction in epilepsy patients

Valproic acid and phenytoin are two prevalent antiepileptic medications known for their narrow indices and propensity for cardiovascular and respiratory system toxicity. Therefore, therapeutic drug monitoring (TDM) of valproic acid (VAL) and phenytoin (PHE) concentrations in patient plasma is extremely beneficial for improving clinical choices, avoiding adverse reactions, and optimizing treatment for individual patients. In this study, a rapid and sensitive ultra-performance liquid chromatographic tandem mass spectrometer (UPLC-MS/MS) method was developed and validated for the simultaneous quantitative determination of valproic acid (VAL) and phenytoin (PHE) in human plasma. Negative electron spray ionization (ESI-) mode with selective ion recording (SIR) was employed to determine the transitions of m/z 142.98 and m/z 250.93 for VAL and PHE, respectively. The internal standard (IS) betamethasone (BETA) was ionized using positive electron spray ionization (ESI+) and detected by multi-reaction monitoring (MRM) mode to obtain precursor ions and specific fragment ions for quantification, and the MRM transition was chosen to be m/z 393.17 → 355.16. The separation was performed using a Phenomenex Synergi Hydro-RP (4 μm, 250 × 4.6 mm, I.D.) with an isocratic mobile phase consisting of acetonitrile – water (75:25, v/v) at a flow rate of 0.8 mL/min. The column temperature was maintained at 25 °C. The lower limit of quantification of VAL and PHE was 3.6 μg/mL and 0.72 μg/mL, respectively, which resulted in a recovery of more than 85 % for most analytes. According to US-FDA bioanalytical technique validation, the specificity, intra- and inter-day precision and accuracy, matrix effect, carryover, dilution, and stability of all analytes were within acceptable ranges. This analytical method was successful in evaluating the levels of valproic acid and phenytoin in human plasma from epileptic patients.

Precise recognition of benzonitrile derivatives with supramolecular macrocycle of phosphorylated cavitand by co-crystallization method

The importance of molecular docking in drug discovery lies in the precise recognition between potential drug compounds and their target receptors, which is generally based on the computational method. However, it will become quite interesting if the rigid cavity structure of supramolecular macrocycles can precisely recognize a series of guests with specific fragments by mimicking molecular docking through co-crystallization experiments. Herein, we report a phenylphosphine oxide-bridged aromatic supramolecular macrocycle, F[3]A1-[P(O)Ph]3, which precisely recognizes benzonitrile derivatives through non-covalent interactions to form key-lock complexes by co-crystallization method. A total of 15 various benzonitrile derivatives as guest molecules are specifically bound by F[3]A1-[P(O)Ph]3 in co-crystal structures, respectively. Notably, among them, crisaborole (anti-dermatitis) and alectinib (anti-cancer) with the benzonitrile fragment, which are two commercial drug molecules approved by the U.S. Food and Drug Administration (FDA), could also form a key-lock complex with F[3]A1-[P(O)Ph]3 in the crystal state, respectively.