Comparative transcriptomic analysis reveals the molecular changes of acute pancreatitis in experimental models

BACKGROUND Acute pancreatitis(AP)encompasses a spectrum of pancreatic inflammatory conditions,ranging from mild inflammation to severe pancreatic necrosis and multisystem organ failure.Given the challenges associated with obtaining human pancreatic samples,research on AP predominantly relies on animal models.In this study,we aimed to elucidate the fundamental molecular mechanisms underlying AP using various AP models.AIM To investigate the shared molecular changes underlying the development of AP across varying severity levels.METHODS AP was induced in animal models through treatment with caerulein alone or in combination with lipopolysaccharide(LPS).Additionally,using Ptf1αto drive the specific expression of the hM3 promoter in pancreatic acinar cells transgenic C57BL/6J-hM3/Ptf1α(cre)mice were administered Clozapine N-oxide to induce AP.Subsequently,we conducted RNA sequencing of pancreatic tissues and validated the expression of significantly different genes using the Gene Expression Omnibus(GEO)database.RESULTS Caerulein-induced AP showed severe inflammation and edema,which were exacerbated when combined with LPS and accompanied by partial pancreatic tissue necrosis.Compared with the control group,RNA sequencing analysis revealed 880 significantly differentially expressed genes in the caerulein model and 885 in the caerulein combined with the LPS model.Kyoto Encyclopedia of Genes and Genomes enrichment analysis and Gene Set Enrichment Analysis indicated substantial enrichment of the TLR and NOD-like receptor signaling pathway,TLR signaling pathway,and NF-κB signaling pathway,alongside elevated levels of apoptosis-related pathways,such as apoptosis,P53 pathway,and phagosome pathway.The significantly elevated genes in the TLR and NOD-like receptor signaling pathways,as well as in the apoptosis pathway,were validated through quantitative real-time PCR experiments in animal models.Validation from the GEO database revealed that only MYD88 concurred in both mouse pancreatic tissue and human AP peripheral blood,while TLR1,TLR7,RIPK3,and OAS2 genes exhibited marked elevation in human AP.The genes TUBA1A and GADD45A played significant roles in apoptosis within human AP.The transgenic mouse model hM3/Ptf1α(cre)successfully validated significant differential genes in the TLR and NOD-like receptor signaling pathways as well as the apoptosis pathway,indicating that these pathways represent shared pathological processes in AP across different models.CONCLUSION The TLR and NOD receptor signaling pathways play crucial roles in the inflammatory progression of AP,notably the MYD88 gene.Apoptosis holds a central position in the necrotic processes of AP,with TUBA1A and GADD45A genes exhibiting prominence in human AP.

Designing Artemisinins with Antimalarial Potential, Combining Molecular Electrostatic Potential, Ligand-Heme Interaction and Multivariate Models

Artemisinins tested against W-2 strains of malaria falciparum are investigated with molecular electrostatic potential (MEP), in an attempt to identify key features of the compounds that are necessary for their activities, as well as to investigate likely interactions with the receptor in a biological process and to use that information to propose new molecules. In order to discover the best geometry involving the ligand-receptor complexes (heme) studied and help in the proposition of the new derivatives, molecular simulations of interactions between the most negative charged region around the peroxide and heme locates (the ones around the Fe2+ ion) were carried out. In addition, PCA (principal components analysis), HCA (hierarchical cluster analysis), SDA (stepwise discriminant analysis), and KNN (K-nearest neighbor) multivariate models were employed to investigate which descriptors are responsible for the classification between the higher and lower antimalarial activity of the compounds, and also this information was used to propose new potentially active molecules. The information accumulated in studies of MEP, molecular docking, and multivariate analysis supported the proposal of new structures with potential antimalarial activities. The multivariate models constructed were applied to the new structures and indicated numbers 19 and 20 as the most prominent for syntheses and biological assays.

Design of N-11-Azaartemisinins Potentially Active against Plasmodium falciparum by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods

N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties εLUMO+1 (one level above lowest unoccupied molecular orbital energy), d(C6-C5) (distance between C6 and C5 atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation.

Outlook of PINK1/Parkin signaling in molecular etiology of Parkinson's disease,with insights into Pink1 knockout models

Parkinson’s disease(PD)relates to defective mitochondrial quality control in the dopaminergic motor network.Genetic studies have revealed that PINK1 and Parkin mutations are indicative of a heightened propensity to PD onset,pinpointing mitophagy and inflammation as the culprit pathways involved in neuronal loss in the substantia nigra(SNpc).In a reciprocal manner,LRRK2 functions in the regulation of basal flux and inflammatory responses responsible for PINK1/Parkin-dependent mitophagy activation.Pharmacological intervention in these diseasemodifying pathways may facilitate the development of novel PD therapeutics,despite the current lack of an established drug evaluation model.As such,we reviewed the feasibility of employing the versatile global Pink1knockout(KO)rat model as a self-sufficient,spontaneous PD model for investigating both disease etiology and drug pharmacology.These rats retain clinical features encompassing basal mitophagic flux changes with PD progression.We demonstrate the versatility of this PD rat model based on the incorporation of additional experimental insults to recapitulate the proinflammatory responses observed in PD patients.

Nanoindentation Models of Monolayer Graphene and Graphyne under Point Load Pattern Studied by Molecular Dynamics

Molecular dynamics simulations are performed to study the nanoindentation models of monolayer suspended graphene and graphyne. Fullerenes are selected as indenters. Our results show that Young＇s modulus of monolayer-thick graphyne is almost half of that of graphene, which is estimated to be 0.50 TPa. The mechanical properties of graphene and graphyne are different in the presence of strain. A pre-tension has an important effect on the mechanical properties of a membrane. Both the pre-tension and Young＇s modulus plots demonstrate index behavior. The toughness of graphyne is stronger than that of graphene due to Young＇s modulus magnitude. Young＇s moduli of graphene and graphyne are almost independent of the size ratio of indenter to membrane.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Network models for molecular kinetics and their initial applications to human health

Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models （MSMs）, are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex couformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer＇s disease.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

DEVELOPMENT OF NEEDLE-BASED MICROENDOSCOPY FOR FLUORESCENCE MOLECULAR IMAGING OF BREAST TUMOR MODELS

Fluorescence molecular imaging enables the visualization of basic molecular processes such as gene expression,enzyme activity,and disease-specific molecular interactions in vivo using targeted contrast agents,and therefore,is being developed for early detection and in situ characterization of breast cancers.Recent advances in developing near-infrared fluorescent imaging contrast agents have enabled the specific labeling of human breast cancer cells in mouse model systems.In synergy with contrast agent development,this paper describes a needle-based fluorescence molecular imaging device that has the strong potential to be translated into clinical breast biopsy procedures.This microendoscopy probe is based on a gradient-index(GRIN)lens interfaced with a laser scanning microscope.Specifications of the imaging performance,including the field-of-view,transverse resolution,and focus tracking characteristics were calibrated.Orthotopic MDA-MB-231 breast cancer xenografts stably expressing the tdTomato red fluorescent protein(RFP)were used to detect the tumor cells in this tumor model as a proof of principle study.With further development,this technology,in conjunction with the development of clinically applicable,injectable fluorescent molecular imaging agents,promises to perform fluorescence molecular imaging of breast cancers in vivo for breast biopsy guidance.

Impact of Vibration Models on Heat Capacities of HF Molecular System

Based on the analysis of completeness and finiteness of HF molecular vibrational levels, HF systemic vibrational heat capacity is studied with quantum statistical and full set of vibrational energy level determined AM (algebraic method). The results show that correct vibrational description and vibrational energy level set of HF system are key factors in calculating heat capacity, HF heat capacity data determined by AM energy spectra {Eυ} are much superior to the ones of harmonic oscillator model, AM results are agreement with experiment data.

Animal models and molecular mechanism of major depressive disorder

Major depressive disorder(MDD)has been a devastating neurological problem in modern history.However,therapeutic strategies to relief the disease are inadequate.The limit in understanding of the molecular mechanism of MDD has been holding back discovery of new therapies.Behind this problem is the establishment of animal models to truly reflect human MDD pathology.In this review,we discuss our current understanding of the molecular mechanism of MDD and the strength and weakness of rodent models of depression.Developing new models of MDD and finding new drugable targets are still important steps to discover new therapies against MDD.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Infrared Spectra and Pyrolysis of Selected Molecular Models of Coal: Insight from Density Functional Calculations

Equilibrium structures and infrared spectra of four typical molecular models of coal have been studied by density functional calculations. Combining theoretical calculations on the coal models with experimental FT-IR spectra of selected low rank perhydrous coals, a plausible molecular representation for this kind of coals was proposed, and its predicted IR spectra reasonably match the experimental observation. Calculations indicate that the cleavage of the C-C bridge bond for the coal structures considered here occurs at about 540 ℃ and the C-O ether bridge bond may break under temperature ranging from 500 to 600 ℃for the aryl-CH2-O-CH2-aryl ether bond or from 200 to 300 ℃ for the aryl-CH2-O-aryl ether bond, showing remarkable effect of the local structural environment. The coal model containing the carboxyl group may release CO2 at about 300 ℃ through the decarboxylation with a barrier of 69 kcal/mol.

MODELS AND MECHANISM OF OPTO-ELECTRONIC MOLECULAR DEVICES

Opto-electronic molecular devices could be classified into three kinds.Theelementary structures of them are presented in this paper.It is pointed out that the elemen-tary excitation theory on charge transfer system can be employed to analyze several impor-tant physical processes in opto-electronic molecular devices.Some basic principles on thedevice design are obtained.The method developed by authors has succeeded in analyzingthe conducting mechanism and switching property of Metal-TCNQ.The crystal structureof Cu-TCNQ and the explanation to the electronic switching effect of Cu-TCNQ are giv-en out first time.

Analysis of the inflection points of height-diameter models

The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.

Progress in experimental models to investigate the in vivo and in vitro antidiabetic activity of drugs

Diabetes mellitus is one of the world's most prevalent and complex metabolic disorders,and it is a rapidly growing global public health issue.It is characterized by hyperglycemia,a condition involving a high blood glucose level brought on by deficiencies in insulin secretion,decreased activity of insulin,or both.Prolonged effects of diabetes include cardiovascular problems,retinopathy,neuropathy,nephropathy,and vascular alterations in both macro-and micro-blood vessels.In vivo and in vitro models have always been important for investigating and characterizing disease pathogenesis,identifying targets,and reviewing novel treatment options and medications.Fully understanding these models is crucial for the researchers so this review summarizes the different experimental in vivo and in vitro model options used to study diabetes and its consequences.The most popular in vivo studies involves the small animal models,such as rodent models,chemically induced diabetogens like streptozotocin and alloxan,and the possibility of deleting or overexpressing a specific gene by knockout and transgenic technologies on these animals.Other models include virally induced models,diet/nutrition induced diabetic animals,surgically induced models or pancreatectomy models,and non-obese models.Large animals or non-rodent models like porcine(pig),canine(dog),nonhuman primate,and Zebrafish models are also outlined.The in vitro models discussed are murine and human beta-cell lines and pancreatic islets,human stem cells,and organoid cultures.The other enzymatic in vitro tests to assess diabetes include assay of amylase inhibition and inhibition ofα-glucosidase activity.

Learning Probabilistic Models of Hydrogen Bond Stability from Molecular Dynamics Simulation Trajectories

Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. H-bonds involving atoms from residues that are close to each other in the main-chain sequence stabilize secondary structure elements. H-bonds between atoms from distant residues stabilize a protein’s tertiary structure. However, H-bonds greatly vary in stability. They form and break while a protein deforms. For instance, the transition of a protein from a non-functional to a functional state may require some H-bonds to break and others to form. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. Other local interactions may reinforce (or weaken) an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of attributes called predictors. A trained model is constructed in the form of a regression tree in which each non-leaf node is a Boolean test (split) on a predictor. Each occurrence of an H-bond maps to a path in this tree from the root to a leaf node. Its predicted stability is associated with the leaf node. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20% better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a given conformation. The paper discusses several extensions that may yield further improvements.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Characterization of Metallic Formation on Ion Exchange/Chelating Resins by Various Metal Groups and Extrapolation to Molecular Models

This paper examines the metallic rare earth element (REE) formations that grow on ion exchange/chelating resins. Formation of these stabilized metallic structures leads to composite particle destruction and appears to be the result of the dynamic environment of the batch experimentation. Polymeric structure, electron availability, pH, kinetic factors, and the REE f-orbitals play significant roles in the formation of the organometallic framework. f-orbitals are largely still not understood to a great extent but this work serves to elucidate the larger role they may play in ligand interactions. Molecular modeling was utilized as a secondary component in investigating rare earth element (REE) deposition onto ion exchange/chelating resins. Modeling of the f-orbital frontier regions and the application of the HOMO-LUMO transition’s effect on molecular transfer and stability is discussed. Advanced metallic loading, in the manner of an organometallic structure, shows short-term stability resulting in particle destruction as increased REE is adsorbed.

Facile synthesis of Cu-doped manganese oxide octahedral molecular sieve for the efficient degradation of sulfamethoxazole via peroxymonosulfate activation

Advanced processes for peroxymonosulfate(PMS)-based oxidation are efficient in eliminating toxic and refractory organic pol-lutants from sewage.The activation of electron-withdrawing HSO_(5)^(-)releases reactive species,including sulfate radical(·SO_(4)^(-)),hydroxyl radical(·OH),superoxide radical(·O_(2)^(-)),and singlet oxygen(1O_(2)),which can induce the degradation of organic contaminants.In this work,we synthesized a variety of M-OMS-2 nanorods(M=Co,Ni,Cu,Fe)by doping Co^(2+),Ni^(2+),Cu^(2+),or Fe^(3+)into manganese oxide oc-tahedral molecular sieve(OMS-2)to efficiently remove sulfamethoxazole(SMX)via PMS activation.The catalytic performance of M-OMS-2 in SMX elimination via PMS activation was assessed.The nanorods obtained in decreasing order of SMX removal rate were Cu-OMS-2(96.40%),Co-OMS-2(88.00%),Ni-OMS-2(87.20%),Fe-OMS-2(35.00%),and OMS-2(33.50%).Then,the kinetics and struc-ture-activity relationship of the M-OMS-2 nanorods during the elimination of SMX were investigated.The feasible mechanism underly-ing SMX degradation by the Cu-OMS-2/PMS system was further investigated with a quenching experiment,high-resolution mass spec-troscopy,and electron paramagnetic resonance.Results showed that SMX degradation efficiency was enhanced in seawater and tap water,demonstrating the potential application of Cu-OMS-2/PMS system in sewage treatment.