MRI-Based Evaluation Improves the Validity of Rodent Nerve Regeneration Studies.

Beam-quality of medical linac evaluations vary by diverse factors. Because conventional beam-quality evaluation methods yield fragmentary results, a new beam-evaluation method is suggested, and its feasibility is evaluated. The PDDs (percentage depth doses) of 6 MV (Mega-voltage) and 10 MV photon, R (Range) of a 6 MeV (Mega Electron-voltage) and 9 MeV electron were measured and compared with the conventional evaluation methods, and the improved methods PDD 10 5 , PDD 20 10 , PDD 30 20 , PDD 20 5 , PDD 30 10 , and R 70 50 , R 50 30 , R 70 30 as the magnetic field of the bending magnet was changed to +2% to ?2%, and the results were compared. The comparison showed that the improved methods exhibit a higher discrimination than the conventional methods in each energy regime. PDD 10 5 , PDD 30 20 , PDD 30 10 and R 70 50 , R 50 30 should be applied. These methods exhibit a higher discrimination in each energy regime than conventional beam-quality evaluation methods; therefore, they should be used for beam-quality evaluation according to the magnetic field variation.

Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g. imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.

We consider an uncertainty evaluation method with uncorrected deflection. In the real uncertainty evaluation, there are a lot of cases which do not correct the deflection and this deflection is simply added to the total uncertainty in the conventional evaluation method. In contrast, we clarify the uncertainty evaluation method with uncorrected deflection along a way of thinking of GUM. The conventional uncertainty evaluation method is overestimated compared with that along a way of thinking of GUM.

Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS classification framework. The proposed approach can serve as a factor-screening tool towards the automatic BRS classification and is promising to be further used at home.

Understanding Why Homeopathic Medicines are Used for Menopause: Searching for Insights into Neuroendocrine Features.

Evaluation of energy saving potential of HVAC system by operation data with uncertainties

Genetic Rescue Reverses Microglial Activation in Preclinical Models of Retinitis Pigmentosa.

Development of an Easily Accessible Patient-Derived Xenograft (PDX) Mouse Model in Multiple Myeloma

Manganese-enhanced MRI reveals changes within brain anxiety and aversion circuitry in rats with chronic neuropathic pain- and anxiety-like behaviors

This article describes procedures for two preclinical animal models for genital herpes infection. The guinea pig model shares many features of genital herpes in humans, including a natural route of inoculation, self-limiting primary vulvovaginitis, spontaneous recurrences, symptomatic and subclinical shedding of HSV-2, and latent infection of the associated sensory ganglia (lumbosacral dorsal root ganglia, DRG). Many humoral and cytokine responses to HSV-2 infection in the guinea pig have been characterized; however, due to the limited availability of immunological reagents, assessments of cellular immune responses are lacking. In contrast, the mouse model has been important in assessing cellular immune responses to herpes infection. Both the mouse and guinea pig models have been extremely useful for evaluating preventative and immunotherapeutic approaches for controlling HSV infection and recurrent disease. In this article, we describe procedures for infecting guinea pigs and mice with HSV-2, scoring subsequent genital disease, and measuring replicating virus to confirm infection. We also provide detailed protocols for dissecting and isolating DRG (the site of HSV-2 latency), quantifying HSV-2 genomic copies in DRG, and assessing symptomatic and subclinical shedding of HSV-2 in the vagina. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Primary and recurrent genital herpes infection in the guinea pig model Support Protocol 1: Blood collection via lateral saphenous vein or by cardiac puncture after euthanasia Support Protocol 2: Dissection and isolation of dorsal root ganglia from guinea pigs Support Protocol 3: PCR amplification and quantification of HSV-2 genomic DNA from samples Basic Protocol 2: Primary genital herpes infection in the mouse model Alternate Protocol: Flank infection with HSV-2 in the mouse model Support Protocol 4: Dissection and isolation of mouse dorsal root ganglia.

Introduction Existing pharmacotherapies for schizophrenia have not progressed beyond targeting dopamine and serotonin neurotransmission. Rodent models of schizophrenia are a necessary tool for elucidating neuropathological processes and testing potential pharmacotherapies, but positive preclinical results in rodent models often do not translate to positive results in the clinic. Areas covered The authors reviewed PubMed for studies that applied rodent behavioral models of schizophrenia to assess the antipsychotic potential of several novel pharmacotherapies currently under investigation. These included acetylcholinesterase inhibitors, muscarinic and nicotinic acetylcholine (ACh) receptor agonists and positive allosteric modulators (PAMs), histamine H3 receptor antagonist/inverse, calcium channel modulators, trace amino acid receptor (TAAR) agonists, and phosphodiesterase 10A (PDE10A) inhibitors. The authors discuss the extent to which the results of preclinical studies of these drugs in rodent models have predicted clinical efficacy. Expert opinion Although published preclinical studies of these drugs were largely positive, clinical results were often modest or negative. This lack of correspondence is likely due to many factors, including differences in experimental design, poor translation of effective dosing from preclinical to clinical studies, and large inter-individual variation of the human population as compared to laboratory rodents. Closing the gap between preclinical and clinical studies will require strategies aimed at reducing the impact of these factors.

BackgroundAnti‐amyloid immunotherapy treatments for early‐stage Alzheimer’s disease (AD) have received FDA approval and are currently in use in the clinic. Vascular sides effects known as Amyloid‐Related Imaging Abnormalities (ARIA) are detected by MRI monitoring and include ARIA‐E (brain edema) and ARIA‐H (microhemorrhages and superficial siderosis). Risk factors for ARIA include carriage of ApoE4 alleles, pre‐existing vascular amyloid (cerebral amyloid angiopathy, CAA), hypertension, pre‐existing microhemorrhages, and anti‐thrombolytic use. Most ARIA‐E occurs early in treatment and is asymptomic however, while rare, severe cases of ARIA have occurred and in some cases were fatal. Because these events occur during treatment and typically are not fatal, it is difficult to discern the underlying pathophysiological changes occurring during and after the resolution of ARIA. Therefore, mouse models of amyloid deposition are used to study the mechanisms of ARIA.MethodThis talk will provide an overview of the mouse models that have been shown to develop ARIA with anti‐amyloid antibody treatment to date and will highlight the advantages as well as the challenges and limitations of these animal models, including the requirement to age mice for 1‐2 years to develop CAA before starting treatment. Newer mouse models that develop CAA faster will be discussed that may help facilitate future ARIA studies in mice.ResultThus far, studies of ARIA in amyloid mouse models have focused mainly on the development of microhemorrhages (ARIA‐H) following chronic immunization. However, MRIs and measures of BBB leakiness and RBC extravasation are now used to monitor the very early effects of anti‐amyloid immunotherapy in mouse brain. Recruitment of immune cells to the brain vasculature, activation of the classical complement cascade, and attempts at vascular remodeling in response to anti‐amyloid antibodies have been demonstrated in mice. Various ‐omics studies suggest potential biomarkers.ConclusionTake together, studies of anti‐amyloid antibody‐induced ARIA in amyloid mouse models have provided insight into the mechanisms underlying ARIA. These models can be used to both identify therapeutic targets and test drugs for efficacy in mitigating ARIA that may be translated into human clinical studies. Funding: NIH NIA 1R01AG084531, NINDS 1R01NS136122‐01 and Cure Alzheimer Fund (CAL).

Mouse models for atopic dermatitis (AD) are an indispensable preclinical research tool for testing new candidate AD therapeutics and for interrogating AD pathobiology in vivo. In this Viewpoint, we delineate why, unfortunately, none of the currently available so-called "AD" mouse models satisfactorily reflect the clinical complexity of human AD, but imitate more "allergic" or "irriant" contact dermatitis conditions. This limits the predictive value of AD models for clinical outcomes of new tested candidate AD therapeutics and the instructiveness of mouse models for human AD pathophysiology research. Here, we propose to initiate a rational debate on the minimal criteria that a mouse model should meet in order to be considered relevant for human AD. We suggest that valid AD models should at least meet the following criteria: (a) an AD-like epidermal barrier defect with reduced filaggrin expression along with hyperproliferation, hyperplasia; (b) increased epidermal expression of thymic stromal lymphopoietin (TSLP), periostin and/or chemokines such as TARC (CCL17); (c) a characteristic dermal immune cell infiltrate with overexpression of some key cytokines such as IL-4, IL-13, IL-31 and IL-33; (d) distinctive "neurodermatitis" features (sensory skin hyperinnervation, defective beta-adrenergic signalling, neurogenic skin inflammation and triggering or aggravation of AD-like skin lesions by perceived stress); and (e) response of experimentally induced skin lesions to standard AD therapy. Finally, we delineate why humanized AD mouse models (human skin xenotransplants on SCID mice) offer a particularly promising preclinical research alternative to the currently available "AD" mouse models.

Preclinical models (typically ovariectomized rats and genetically altered mice) have underpinned much of what we know about skeletal biology. They have been pivotal for developing therapies for osteoporosis and monogenic skeletal conditions, including osteogenesis imperfecta, achondroplasia, hypophosphatasia, and craniodysplasias. Further therapeutic advances, particularly to improve cortical strength, require improved understanding and more rigorous use and reporting. We describe here how trabecular and cortical bone structure develop, are maintained, and degenerate with aging in mice, rats, and humans, and how cortical bone structure is changed in some preclinical models of endocrine conditions (eg, postmenopausal osteoporosis, chronic kidney disease, hyperparathyroidism, diabetes). We provide examples of preclinical models used to identify and test current therapies for osteoporosis, and discuss common concerns raised when comparing rodent preclinical models to the human skeleton. We focus especially on cortical bone, because it differs between small and larger mammals in its organizational structure. We discuss mechanisms common to mouse and human controlling cortical bone strength and structure, including recent examples revealing genetic contributors to cortical porosity and osteocyte network configurations during growth, maturity, and aging. We conclude with guidelines for clear reporting on mouse models with a goal for better consistency in the use and interpretation of these models.

Molecule-targeted therapies are being widely developed and deployed, but they are frequently less effective in clinical trials than predicted based upon preclinical studies. Frequently, only a single model or genetic background is utilized using diets that are not relevant to that consumed by most cancer patients, which may contribute to the lack of predictability of many preclinical therapeutic studies. Inhibition of epidermal growth factor receptor (EGFR) in colorectal cancer was used to investigate potential causes for low predictive values of many preclinical studies. The efficacy of the small molecule EGFR inhibitor AG1478 was evaluated using two mouse models, ApcMin/+ and azoxymethane (AOM), both sexes on three genetic backgrounds, C57BL/6J (B6) and A/J (A) inbred strains and AB6F1 hybrids, and two diets, standard chow (STD) or Western-style diet (WD). AG1478 has significant anti-tumor activity in the B6-ApcMin/+ model with STD but only moderately on the WD and in the AOM model on an A background with a WD but not STD. On the F1 hybrid background AG1478 is effective in the ApcMin/+ model with either STD or WD, but has only moderate efficacy in the AOM model with either diet. Sex differences were also observed. Unexpectedly, the level of liver EGFR phosphorylation inhibition by AG1478 was not positively correlated with inhibition of tumor growth in the AOM model. Model-dependent interactions between genetic background and diet can dramatically impact preclinical results, and indicate that low predictive values of preclinical studies can be attributed to study designs that do not account for the heterogeneous patient population or the diets they consume. Better-designed preclinical studies should lead to more accurate predictions of therapeutic response in the clinic.