Revealing the mechanism of action of a first-in-class covalent inhibitor of KRASG12C (ON) and other functional properties of oncogenic KRAS by 31P NMR.

Individual oncogenic KRAS mutants confer distinct differences in biochemical properties and signaling for reasons that are not well understood. KRAS activity is closely coupled to protein dynamics and is regulated through two interconverting conformations: state 1 (inactive, effector binding deficient) and state 2 (active, effector binding enabled). Here, we use 31P NMR to delineate the differences in state 1 and state 2 populations present in WT and common KRAS oncogenic mutants (G12C, G12D, G12V, G13D, and Q61L) bound to its natural substrate GTP or a commonly used nonhydrolyzable analog GppNHp (guanosine-5'-[(ß,γ)-imido] triphosphate). Our results show that GppNHp-bound proteins exhibit significant state 1 population, whereas GTP-bound KRAS is primarily (90% or more) in state 2 conformation. This observation suggests that the predominance of state 1 shown here and in other studies is related to GppNHp and is most likely nonexistent in cells. We characterize the impact of this differential conformational equilibrium of oncogenic KRAS on RAF1 kinase effector RAS-binding domain and intrinsic hydrolysis. Through a KRAS G12C drug discovery, we have identified a novel small-molecule inhibitor, BBO-8956, which is effective against both GDP- and GTP-bound KRAS G12C. We show that binding of this inhibitor significantly perturbs state 1-state 2 equilibrium and induces an inactive state 1 conformation in GTP-bound KRAS G12C. In the presence of BBO-8956, RAF1-RAS-binding domain is unable to induce a signaling competent state 2 conformation within the ternary complex, demonstrating the mechanism of action for this novel and active-conformation inhibitor.

scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.

Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering algorithms, which project single-cell expression data into a lower dimensional space and then cluster cells based on their distances from each other. However, as these methods do not use reference datasets, they can only achieve a rough classification of cell-types, and it is difficult to improve the recognition accuracy further. To effectively solve this issue, we propose a novel supervised annotation method, scDeepInsight. The scDeepInsight method is capable of performing manifold assignments. It is competent in executing data integration through batch normalization, performing supervised training on the reference dataset, doing outlier detection and annotating cell-types on query datasets. Moreover, it can help identify active genes or marker genes related to cell-types. The training of the scDeepInsight model is performed in a unique way. Tabular scRNA-seq data are first converted to corresponding images through the DeepInsight methodology. DeepInsight can create a trainable image transformer to convert non-image RNA data to images by comprehensively comparing interrelationships among multiple genes. Subsequently, the converted images are fed into convolutional neural networks such as EfficientNet-b3. This enables automatic feature extraction to identify the cell-types of scRNA-seq samples. We benchmarked scDeepInsight with six other mainstream cell annotation methods. The average accuracy rate of scDeepInsight reached 87.5%, which is more than 7% higher compared with the state-of-the-art methods.

Predicting lysine methylation sites using a convolutional neural network.

Protein lysine methylation is a particular type of post translational modification that plays an important role in both histone and non-histone function regulation in proteins. Deregulation caused by lysine methyltransferases has been identified as the cause of several diseases including cancer as well as both mental and developmental disorders. Identifying lysine methylation sites is a critical step in both early diagnosis and drug design. This study proposes a new Machine Learning method called CNN-Meth for predicting lysine methylation sites using a convolutional neural network (CNN). Our model is trained using evolutionary, structural, and physicochemical-based presentation along with binary encoding. Unlike previous studies, instead of extracting handcrafted features, we use CNN to automatically extract features from different presentations of amino acids to avoid information loss. Automated feature extraction from these representations of amino acids as well as CNN as a classifier have never been used for this problem. Our results demonstrate that CNN-Meth can significantly outperform previous methods for predicting methylation sites. It achieves 96.0%, 85.1%, 96.4%, and 0.65 in terms of Accuracy, Sensitivity, Specificity, and Matthew's Correlation Coefficient (MCC), respectively. CNN-Meth and its source code are publicly available at https://github.com/MLBC-lab/CNN-Meth.

Apolipoprotein E is enriched in dense deposits and is a marker for dense deposit disease in C3 glomerulopathy.

C3 glomerulopathy (C3G) is a rare disease resulting from dysregulation of the alternative pathway of complement. C3G includes C3 glomerulonephritis (C3GN) and dense deposit disease (DDD), both of which are characterized by bright glomerular C3 staining on immunofluorescence studies. However, on electron microscopy (EM), DDD is characterized by dense osmiophilic mesangial and intramembranous deposits along the glomerular basement membranes (GBM), while the deposits of C3GN are not dense. Why the deposits appear dense in DDD and not in C3GN is not known. We performed laser microdissection (LCM) of glomeruli followed by mass spectrometry (MS) in 12 cases each of DDD, C3GN, and pretransplant kidney control biopsies. LCM/MS showed marked accumulation of complement proteins C3, C5, C6, C7, C8, C9 and complement regulating proteins CFHR5, CFHR1, and CFH in C3GN and DDD compared to controls. C3, CFH and CFHR proteins were comparable in C3GN and DDD. Yet, there were significant differences. First, there was a six-to-nine-fold increase of C5-9 in DDD compared to C3GN. Secondly, an unexpected finding was a nine-fold increase in apolipoprotein E (ApoE) in DDD compared to C3GN. Most importantly, immunohistochemical and confocal staining for ApoE mirrored the dense deposit staining in the GBM in DDD but not in C3GN or control cases. Validation studies using 31 C3G cases confirmed the diagnosis of C3GN and DDD in 80.6 % based on ApoE staining. Overall, there is a higher burden of terminal complement pathway proteins in DDD compared to C3GN. Thus, our study shows that dense deposits in DDD are enriched with ApoE compared to C3GN and control cases. Hence, ApoE staining may be used as an adjunct to EM for the diagnosis of DDD and might be valuable when EM is not available.

Advances in AI and machine learning for predictive medicine.

The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.

Neuroprotective Action of Selected Natural Drugs Against Neurological Diseases and Mental Disorders: Potential Use Against Radiation Damage.

Exposure to radiation, ionizing and non-ionizing radiation, is a significant concern in modern society. The brain is the organ that is most sensitive to radiation exposure. This review describes how exposure to radiation can affect neurotransmitters in different brain regions, affecting brain function. This review covers neurodegenerative diseases such as Alzheimer's, Parkinson's, and neuroinflammation due to changes in neurons in the central nervous system, and the effects thereon of medicinal plants such as Allium cepa, Allium sativum, Centella asiatica, Coriandrum sativum, and Crocus sativus plants, used for centuries in traditional medicine. These herbal medicines exert free radical scavenging, and antioxidant as well as anti-inflammatory properties which can be beneficial in managing neurological diseases. The present review compiles the neuroprotective effects of selected natural plants against neurological damage, as well as highlights the different mechanisms of action elicited to induce and produce beneficial effects. The current review describes recent studies on the pharmacological effects of neuroprotective herbs on various neurological and mental illnesses, and shows the way further studies can impact this field, including potential effects on radiation-induced damage.

Membranous Nephropathy in Syphilis is Associated with Neuron-Derived Neurotrophic Factor.

SIGNIFICANCE STATEMENT: Syphilis is a common worldwide sexually transmitted infection. Proteinuria may occur in patients with syphilis. Membranous nephropathy (MN) is the most common cause of proteinuria in syphilis. The target antigen of MN in syphilis is unknown. This study shows that MN in syphilis is associated with a novel target antigen called neuron-derived neurotrophic factor (NDNF). NDNF-associated MN has distinctive clinical and pathologic manifestations and NDNF appears to be the target antigen in syphilis-associated MN. BACKGROUND: Syphilis is a common sexually transmitted infection. Membranous nephropathy (MN) is a common cause of proteinuria in syphilis. The target antigen is not known in most cases of syphilis-associated MN. METHODS: We performed laser microdissection of glomeruli and mass spectrometry (MS/MS) in 250 cases (discovery cohort) of phospholipase A2 receptor-negative MN to identify novel target antigens. This was followed by immunohistochemistry/confocal microscopy to localize the target antigen along the glomerular basement membrane (GBM). Western blot analyses using IgG eluted from frozen biopsy tissue were performed to detect binding to target antigen. RESULTS: MS/MS studies of the discovery cohort revealed high total spectral counts of a novel protein, neuron-derived neurotrophic factor (NDNF), in three patients: one each with syphilis and hepatitis B, HIV (syphilis status not known), and lung tumor. Next, MS/MS studies of five cases of syphilis-MN (validation cohort) confirmed high total spectral counts of NDNF (average 45±20.4) in all (100%) cases. MS/MS of 14 cases of hepatitis B were negative for NDNF. All eight cases of NDNF-associated MN were negative for known MN antigens. Electron microscopy showed stage I MN in all cases, with superficial and hump-like deposits without GBM reaction. IgG1 was the dominant IgG subtype on MS/MS and immunofluorescence microscopy. Immunohistochemistry/confocal microscopy showed granular staining and colocalization of NDNF and IgG along GBM. Western blot analyses using eluate IgG of NDNF-MN showed binding to both nonreduced and reduced NDNF, while IgG eluate from phospholipase A2 receptor-MN showed no binding. CONCLUSION: NDNF is a novel antigenic target in syphilis-associated MN.

Comprehensive Review on Herbal Medicine: Emphasis on Current Therapy and Role of Phytoconstituents for Cancer Treatment.

INTRODUCTION: Cancer poses a significant public health challenge in both developed and developing nations, with a rising global incidence of patients facing the threat of death due to abnormal cell proliferation. AIM: Review explores the utilization of different parts of herbal medicinal plants and their active pharmaceutical constituents in the prevention and treatment of various types of cancer. METHODOLOGY: Various anticancer medicinal plants have been identified, demonstrating their therapeutic effects by inhibiting cancer-stimulating enzymes and hormones, activating DNA repair processes, boosting the synthesis of protective stimulants, reducing the formation of free radicals, and enhancing individual immunity. Data for this study were gathered from diverse online bibliographic and databases, including Google, Google Scholar, Mendeley, Springer Link, Research Gate, and PubMed. RESULT: Herbal drugs have a huge contribution to the inhibition of the progression of cancer.A large volume of clinical studies has reported the beneficial effects of herbal medicines on the survival, immune modulation, and quality of life (QOL) of cancer patients, when these herbal medicines are used in combination with conventional therapeutics. CONCLUSION: The latest medicines for the clinical purpose (Above 50 %) are derived from herbal products. Furthermore, combination of these herbs with nanotechnology shows promise in treating specific carcinomas.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.

Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.

Spontaneous histopathology in New Zealand White rabbits: ten years of control data.

The historical control database of a multinational laboratory services provider was queried for all histopathologic findings in New Zealand White rabbits which were used as control animals during a ten-year period (2011-2020). The query included all evaluated tissues, with or without microscopic findings, in studies conducted for safety testing for regulatory approval by the U.S. Food and Drug Agency (FDA) or the U.S. Environmental Protection Agency. A second query included studies conducted in the United Kingdom for control rabbits used in studies compliant with the Healthcare Products Regulatory Agency (MHRA) and/or the European Medicines Agency (EMA), which provide regulatory oversight in the United Kingdom and European Union, respectively. Infiltrates of inflammatory (mixed or mononuclear) cells were commonly noted in various organs including heart, digestive tract, muscle, thyroid, kidney, urinary bladder, eyelid, ocular structures, harderian gland, lacrimal gland, and lung. Mineralization was noted in aorta, kidney, urinary bladder, and ovary. Also noted were degeneration/necrosis in the myocardium, and intramuscular injection sites of the skin, degeneration/regeneration of muscle and diaphragm, ectopic tissue in the pancreas and thyroid, basophilic foci in salivary gland, increased/decreased vacuolation in adrenal gland, increased/decreased lymphocytic cellularity of lymph nodes, intrasinusoidal erythrocytes in lymph nodes, thymic atrophy, increased adipocytes in bone marrow, inflammatory cell foci in the liver and gall bladder, lacrimal gland atrophy, renal tubule basophilia, degeneration/regeneration, and dilatation; oviduct cyst; in the testis, degeneration/atrophy, cellular debris, dilatation, decreased sperm and segmental hypoplasia of seminiferous tubules; and squamous metaplasia of the testis and seminal vesicle.

Severity of muscle impairment and its progression assessed using musculoskeletal magnetic resonance imaging and diffusion tension imaging in 78 boys with Duchenne muscular dystrophy: a retrospective study.

Purpose: Duchenne muscular dystrophy (DMD) is the most common and severe form of muscular dystrophy. Current diagnostic tests like genetic testing, needle electromyography, and muscle biopsy are either not easily available or invasive, and they are impractical for assessing disease progression and treatment outcomes. Therefore, there is a need for a non-invasive and accurate investigative modality for DMD. In recent years, musculoskeletal magnetic resonance imaging (MRI-MSK) along with fractional anisotropy (FA) and diffusion tensor imaging (DTI) have become major non-invasive tools. Material and methods: T1-weighted MRI-MSK and FA measures of DTI of 78 DMD patients were retrospectively studied to identify the distinct pattern of muscle involvement and fatty infiltration as age and/or disease progresses. Correlation analysis was performed between MRI-MSK grade score vs. age, muscle strength, and Vignos scale. Spearman's rank correlation coefficient was used. Results: As age increased, the MRI grade score and Vignos score increased. There was a statistically significant high positive correlation between MRI-MSK grade score and age, and low positive correlation with Vignos scores. With increasing age, the muscle strength on manual muscle testing (MMT) and FA value decreased. There was high negative correlation with muscle strength on MMT and low positive correlation between FA values and MMT score. Conclusions: On T1-weighted MRI, a distinct pattern, extent, and distribution of lower limb muscle involvement can be seen. MRI-MSK grade score worsens with progressing age, reducing strength, and increasing functional impairment. FA alone may not be an accurate marker in assessing progression of DMD. MRI-MSK and other DTI measures should be further explored as diagnostic and prognostic tools for DMD.

FEATS: feature selection-based clustering of single-cell RNA-seq data.

MOTIVATION: Advances in next-generation sequencing have made it possible to carry out transcriptomic studies at single-cell resolution and generate vast amounts of single-cell RNA sequencing (RNA-seq) data rapidly. Thus, tools to analyze this data need to evolve as well as to improve accuracy and efficiency. RESULTS: We present FEATS, a Python software package, that performs clustering on single-cell RNA-seq data. FEATS is capable of performing multiple tasks such as estimating the number of clusters, conducting outlier detection and integrating data from various experiments. We develop a univariate feature selection-based approach for clustering, which involves the selection of top informative features to improve clustering performance. This is motivated by the fact that cell types are often manually determined using the expression of only a few known marker genes. On a variety of single-cell RNA-seq datasets, FEATS gives superior performance compared with the current tools, in terms of adjusted Rand index and estimating the number of clusters. It achieves a 22% improvement in clustering and more accurately estimates the number of clusters when compared with other tools. In addition to cluster estimation, FEATS also performs outlier detection and data integration while giving an excellent computational performance. Thus, FEATS is a comprehensive clustering tool capable of addressing the challenges during the clustering of single-cell RNA-seq data. AVAILABILITY: The installation instructions and documentation of FEATS is available at https://edwinv87.github.io/feats/. SUPPLEMENTARY DATA: Supplementary data are available online at https://academic.oup.com/bib.

DeepFeature: feature selection in nonimage data using convolutional neural network.

Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests. Among these methods, deep neural networks are currently among the most advanced approaches and, in particular, convolutional neural networks (CNNs) have been shown to perform excellently for a variety of difficult tasks. Despite that applications of this type of networks to high-dimensional omics data and, most importantly, meaningful interpretation of the results returned from such models in a biomedical context remains an open problem. Here we present, an approach applying a CNN to nonimage data for feature selection. Our pipeline, DeepFeature, can both successfully transform omics data into a form that is optimal for fitting a CNN model and can also return sets of the most important genes used internally for computing predictions. Within the framework, the Snowfall compression algorithm is introduced to enable more elements in the fixed pixel framework, and region accumulation and element decoder is developed to find elements or genes from the class activation maps. In comparative tests for cancer type prediction task, DeepFeature simultaneously achieved superior predictive performance and better ability to discover key pathways and biological processes meaningful for this context. Capabilities offered by the proposed framework can enable the effective use of powerful deep learning methods to facilitate the discovery of causal mechanisms in high-dimensional biomedical data.

Multifaceted Neuroprotective Role of Punicalagin: A Review.

Millions of people worldwide are currently afflicted with neurologic conditions like a seizure, depression, stress, Alzheimer's disease, Parkinson's disease, and Huntington's disease. However, the precise etiopathology of these diseases is still unknown. Substantial studies are being conducted to discover more treatments against these disorders because many patients do not experience the therapeutic benefits that would be expected from using existing pharmaceutical strategies. Herbal medicines which have been used in traditional medicine for millennia to treat various neurological problems are also being investigated and scientifically assessed. Punicalagin is a known polyphenol that has significant antioxidant, anti-inflammatory, anti-viral, anti-proliferative, and anti-cancer properties. Around the world, traditional use of herbal drugs is gaining wider acceptance as a part of complementary and alternative medicine. The scientific community should pay attention to these many neuroprotective pharmacodynamic activities of Punicalagin to create effective pharmacotherapeutic plans, as evidenced by mounting data in pre-clinical research investigations. The current review describes the recent studies on the pharmacological effects of Punicalagin in a variety of neurological illnesses and paves the way for further study in this field.

Compendium of naringenin: potential sources, analytical aspects, chemistry, nutraceutical potentials and pharmacological profile.

Naringenin is flavorless, water insoluble active principle belonging to flavanone subclass. It exhibits a diverse pharmacological profile as well as divine nutraceutical values. Although several researchers have explored this phytoconstituent to evaluate its promising properties, still it has not gained recognition at therapeutic levels and more clinical investigations are still required. Also the neutraceutical potential has limited marketed formulations. This compilation includes the description of reported therapeutic potentials of naringenin in variety of pathological conditions alongwith the underlying mechanisms. Details of various analytical investigations carried on this molecule have been provided along with brief description of chemistry and structural activity relationship. In the end, various patents filed and clinical trial data has been provided. Naringenin has revealed promising pharmacological activities including cardiovascular diseases, neuroprotection, anti-diabetic, anticancer, antimicrobial, antiviral, antioxidant, anti-inflammatory and anti-platelet activity. It has been marketed in the form of nanoformulations, co-crystals, solid dispersions, tablets, capsules and inclusion complexes. It is also available in various herbal formulations as nutraceutical supplement. There are some pharmacokinetic issue with naringenin like poor absorption and low dissolution rate. Although these issues have been sorted out upto certain extent still further research to investigate the bioavailability of naringenin from herbal supplements and its clinical efficacy is essential.

A comprehensive compiled review is presented on source, health benefits, chemistry and analysis, and marketed products of naringenin.Naringenin is a promising phytoconstituent as nutraceutical.Valorization of fruit peels of citrus fruits is a critical step for food and nutraceutical industry.Structure-activity relationship of naringenin derivatives.Nano-formulations incorporating naringenin in themselves can be used for targeted delivery for critical disorders.Naringenin obtained from the peels can be efficiently used in breads, cookies, cakes and other food products.

Historical Control Background Incidence of Spontaneous Neoplastic Lesions of Sprague-Dawley Rats in 104-Week Toxicity Studies.

Data collected from approximately 1800 male and 1800 female Sprague-Dawley (SD) rats used in 104-week carcinogenicity studies were archived in a historical control database at Labcorp Early Development, Inc, and the neoplastic microscopic observation data from these rats were retrospectively evaluated. Historical control data can provide useful information on the range and incidence of spontaneously occurring background neoplasms in the species and strain of the test animal used in different types of toxicity studies, including studies of differing lengths, delivery of test article, and test animal. Some of the most common malignant findings noted included fibrosarcoma of skin/subcutis and thyroid C-cell carcinoma in males (2.1% each) while mammary gland carcinoma and pituitary carcinoma (25% and 2.6%) were most common in females. Pituitary adenoma of pars distalis was found to be the most prevalent benign neoplasm in both males and females (56.4% and 77.1%). Fibroadenoma of mammary gland (35.6%) and thyroid C-cell adenoma (8.5%) were the second and third most common benign tumors in female SD rats. In males, the thyroid C-cell adenoma (10.9%) and benign pheochromocytoma (8.9%) were the second and third most common tumors.

Scientific and Regulatory Policy Committee Points to Consider: Sampling, Processing, Evaluation, Interpretation, and Reporting of Test Article-Related Ganglion Pathology for Nonclinical Toxicity Studies.

Certain biopharmaceutical products consistently affect dorsal root ganglia, trigeminal ganglia, and/or autonomic ganglia. Product classes targeting ganglia include antineoplastic chemotherapeutics, adeno-associated virus-based gene therapies, antisense oligonucleotides, and anti-nerve growth factor agents. This article outlines "points to consider" for sample collection, processing, evaluation, interpretation, and reporting of ganglion findings; these points are consistent with published best practices for peripheral nervous system evaluation in nonclinical toxicity studies. Ganglion findings often occur as a combination of neuronal injury (e.g., degeneration, necrosis, and/or loss) and/or glial effects (e.g., increased satellite glial cell cellularity) with leukocyte accumulation (e.g., mononuclear cell infiltration or inflammation). Nerve fiber degeneration and/or glial reactions may be seen in nerves, dorsal spinal nerve roots, spinal cord, and occasionally brainstem. Interpretation of test article (TA)-associated effects may be confounded by incidental background changes or experimental procedure-related changes and limited historical control data. Reports should describe findings at these sites, any TA relationship, and the criteria used for assigning severity grades. Contextualizing adversity of ganglia findings can require a weight-of-evidence approach because morphologic changes of variable severity occur in ganglia but often are not accompanied by observable overt in-life functional alterations detectable by conventional behavioral and neurological testing techniques.

Scientific and Regulatory Policy Committee Technical Review: Biology and Pathology of Ganglia in Animal Species Used for Nonclinical Safety Testing.

Dorsal root ganglia (DRG), trigeminal ganglia (TG), other sensory ganglia, and autonomic ganglia may be injured by some test article classes, including anti-neoplastic chemotherapeutics, adeno-associated virus-based gene therapies, antisense oligonucleotides, nerve growth factor inhibitors, and aminoglycoside antibiotics. This article reviews ganglion anatomy, cytology, and pathology (emphasizing sensory ganglia) among common nonclinical species used in assessing product safety for such test articles (TAs). Principal histopathologic findings associated with sensory ganglion injury include neuron degeneration, necrosis, and/or loss; increased satellite glial cell and/or Schwann cell numbers; and leukocyte infiltration and/or inflammation. Secondary nerve fiber degeneration and/or glial reactions may occur in nerves, dorsal spinal nerve roots, spinal cord (dorsal and occasionally lateral funiculi), and sometimes the brainstem. Ganglion findings related to TA administration may result from TA exposure and/or trauma related to direct TA delivery into the central nervous system or ganglia. In some cases, TA-related effects may need to be differentiated from a spectrum of artifactual and/or spontaneous background changes.

The Role of Ultrasound in Cancer and Cancer-Related Pain-A Bibliometric Analysis and Future Perspectives.

Ultrasound has a deep penetrating ability with minimal or no tissue injury, while cancer-mediated complications during diagnosis, therapy, and surgery have become a serious challenge for clinicians and lead to the severity of the primary condition (cancer). The current study highlights the importance of ultrasound imaging and focused ultrasound therapy during cancer diagnosis, pain reduction, guidance for surgical resection of cancer, and the effectiveness of chemotherapy. We performed the bibliometric analysis on research domains involving ultrasound, cancer management, pain, and other challenges (chemotherapy, surgical guidance, and postoperative care), to observe the trend by which the research field has grown over the years and propose a possible future trend. The data was obtained from the Web of Science, processed, and exported as plain text files for analysis in the Bibliometrix R web interface using the Biblioshiny package. A total of 3248 documents were identified from 1100 journal sources. A total of 390 articles were published in 2022, with almost a 100% growth rate from previous years. Based on the various network analysis, we conclude that the outcome of the constant research in this domain will result in better patient care during the management of various diseases, including cancer and other co-morbidities.