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.

Structural Heart Imaging Using 3-Dimensional Intracardiac Echocardiography: JACC Cardiovascular Imaging Position Statement.

3-dimensional (3D) intracardiac echocardiography (ICE) is emerging as a promising complement and potential alternative to transesophageal echocardiography for imaging guidance in structural heart interventions. To establish standardized practices, our multidisciplinary expert position statement serves as a comprehensive guide for the appropriate indications and utilization of 3D-ICE in various structural heart procedures. The paper covers essential aspects such as the fundamentals of 3D-ICE imaging, basic views, and workflow recommendations specifically tailored for ICE-guided structural heart procedures, such as transeptal puncture, device closure of intracardiac structures, and transcatheter mitral and tricuspid valve interventions. Current challenges, future directions, and training requirements to ensure operator proficiency are also discussed, thereby promoting the safety and efficacy of this innovative imaging modality to support expanding its future clinical applications.

Enhanced analysis of tabular data through Multi-representation DeepInsight.

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields.

Abnormal Development of the Corpus Callosum in Autism Spectrum Disorder: An MRI Study.

BACKGROUND: Altered size in the corpus callosum (CC) has been reported in individuals with autism spectrum disorder (ASD), but few studies have investigated younger children. Moreover, knowledge about the age-related changes in CC size in individuals with ASD is limited. OBJECTIVES: Our objective was to investigate the age-related size of the CC and compare them with age-matched healthy controls between the ages of 2 and 18 years. METHODS: Structural-weighted images were acquired in 97 male patients diagnosed with ASD; published data were used for the control group. The CC was segmented into 7 distinct subregions (rostrum, genu, rostral body, anterior midbody, posterior midbody, isthmus, and splenium) as per Witelson's technique using ITK-SNAP software. We calculated both the total length and volume of the CC as well as the length and height of its 7 subregions. The length of the CC measures was studied as both continuous and categorical forms. For the continuous form, Pearson's correlation was used, while categorical forms were based on age ranges reflecting brain expansion during early postnatal years. Differences in CC measures between adjacent age groups in individuals with ASD were assessed using a Student t-test. Mean and standard deviation scores were compared between ASD and control groups using the Welch t-test. RESULTS: Age showed a moderate positive association with the total length of the CC (r = 0.43; Padj = 0.003) among individuals with ASD. Among the subregions, a positive association was observed only in the anterior midbody of the CC (r = 0.41; Padj = 0.01). No association was found between the age and the height of individual subregions or with the total volume of the CC. In comparison with healthy controls, individuals with ASD exhibited shorter lengths and heights of the genu and splenium of the CC across wide age ranges. CONCLUSION: Overall, our results highlight a distinct abnormal developmental trajectory of CC in ASD, particularly in the genu and splenium structures, potentially reflecting underlying pathophysiological mechanisms that warrant further investigation.

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.

Efficacy of an online lung ultrasound module on skill acquisition by clinician: a new paradigm.

Introduction: Lung ultrasound (LUS) as an assessment tool has seen significant expansion in adult, paediatric, and neonatal populations due to advancements in point-of-care ultrasound over the past two decades. However, with fewer experts and learning platforms available in low- and middle-income countries and the lack of a standardised supervised training programme, LUS is not currently effectively used to the best of its potential in neonatal units. Methodology: A cross-sectional survey assessed the efficacy of learning LUS via a mentor-based online teaching module (NEOPOCUS). The questionnaire comprised the clinicians' demographic profile, pre-course skills, and self-assessment of skill acquisition after course completion with ongoing hands-on practice. Results: A total of 175 clinicians responded to the survey, with the majority (87.9%) working in level 3 and 4 neonatal intensive care units. Clinicians had variable clinical experience. Of them, 53.2% were consultant paediatricians/neonatologists with over 10 years of experience. After the course, there was a significant increase in clinician confidence levels in diagnosing and assessing all LUS pathology, as evidenced by the increase in median cumulative scores [from baseline 6 (interquartile range, IQR, 6-9) to 20 (IQR 16-24), p < 0.001] with half of them gaining confidence within 3 months of the course. Conclusion: An online curriculum-based neonatal lung ultrasound training programme with clinician image demonstration and peer review of images for image optimisation increases self-reported confidence in diagnosing and managing neonatal lung pathology. Web-based online training in neonatal lung ultrasound has merits that can help with the delivery of training globally, and especially in low- and middle-income countries.

Challenges and solutions to manufacturing of low viscosity, ultra-high concentration IgG1 drug products: From late downstream process to final fill finish processing.

Challenges in manufacturing of high concentration antibody formulations have seldom been discussed. These are observed mainly form late downstream operations where antibody gets concentrated to its final strength, to final fill finish processing and containerization of the product. Present paper summarizes challenges typically observed in manufacturing and processing of high concentration antibody products and provides turnkey solutions to these typical challenges in order to have their consistent and robust manufacturing process. IgG1 has been used as model protein for studying the challenges and providing solutions to them. The late downstream challenges like increased viscosity limiting further concentration can be resolved by used of viscosity modifying agents in the formulation. Replacement of conventionally used 'A' screen membranes with 'D' screen or using single pass TFF can further provide advantage in targeting higher concentrations for same protein with lesser shear and aggregation. Using 0.5µm/0.2µm asymmetric or bilayered membrane instead of conventional 0.2µm membrane resulted in better flux while filtration of high concentration IgG1 formulation. In process holding time during filling operation was optimized to be <60min based on the nozzle drying time for high concentration IgG1 formulation. Appropriate control strategy of replacing filling nozzles and performing periodic fill weight check was proposed for fill finish process of high concentration IgG1 formulation.

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.

Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.

Promising Potential of Curcumin and Related Compounds for Antiviral Drug Discovery.

Viruses are acellular, microscopic, and mobile particles containing genetic particles, either DNA/RNA strands as nucleoproteins, responsible for 69,53,743 deaths till the year 2023. Curcumin and related compounds are among the areas of pivotal interest for researchers because of their versatile pharmacological profile. Chemically known as diferuloylmethane, which is a main constituent of turmeric along with demethoxycurcumin and bisdemethoxycurcumin, they have a broad spectrum of antiviral activity against viruses such as human immunodeficiency virus, herpes simplex virus, influenza virus (Avian influenza) and Hepatitis C virus HIV. The possible role of curcumin as an antiviral agent may be attributed to the activation of the 20S proteasome, a cellular machinery responsible for degrading unfolded or misfolded proteins in a ubiquitin-independent manner. It shows suppression of HBV entry at various infection stages by inhibiting cccDNA replication by inhibiting the Wnt/ß-catenin signaling pathway to attenuate IAV-induced myocarditis.

Nephrotic Syndrome and Posterior Reversible Encephalopathy Syndrome as Clinical Presentations of Gemcitabine-Induced Thrombotic Micro-Angiopathy.

Gemcitabine-induced thrombotic micro-angiopathy (GiTMA) is a very rare pathology of micro-vascular occlusion with a poor prognosis. In this case report, we present a young male with pancreatic carcinoma who received gemcitabine as adjuvant chemotherapy and developed thrombotic micro-angiopathy (TMA) manifesting as nephrotic syndrome with renal dysfunction and posterior reversible encephalopathy syndrome (PRES). The case was successfully managed with discontinuation of the drug and conservative management. The pathogenesis of GiTMA might be direct endothelial dysfunction with consequent activation of the clotting system. The role of plasma exchanges and monoclonal antibodies is unclear in drug-induced TMA.

Successful Reversal of Refractory Posttransplant Thrombotic Microangiopathy with Eculizumab.

Posttransplant thrombotic microangiopathy (PT-TMA) can be caused by calcineurin inhibitors (CNIs), ischemic injury, infections, or antibody-mediated rejection (ABMR). Delayed recognition can result in allograft loss. We describe the first reported case of successful reversal of refractory PT-TMA with eculizumab in India. It highlights the importance of prompt diagnosis and benefit from an early initiation of eculizumab therapy in refractory cases.

Antimicrobial Potential of Polyphenols: An Update on Alternative for Combating Antimicrobial Resistance.

The last decade has encountered an increasing demand for plant-based natural antibiotics. This demand has led to more research-based investigations for natural sources of antimicrobial agents and published reports demonstrating that plant extracts are widely applied in modern medicine, reporting potential activity that may be due to polyphenol compounds. Interestingly, the effects of polyphenols on the sensitivity of bacteria to antibiotics have not been well-studied. Hence, the current review encompasses the prospective application of plant-based phenolic extracts from plants of Indian origin. The emergence of resistance to antimicrobial agents has increased the inefficacy of many antimicrobial drugs. Several strategies have been developed in recent times to overcome this issue. A combination of antimicrobial agents is employed for the failing antibiotics, which restores the desirable effect but may have toxicity-related issues. Phytochemicals such as some polyphenols have demonstrated their potent activity as antimicrobial agents of natural origin to work against resistance issues. These agents alone or in combination with certain antibiotics have been shown to enhance the antimicrobial activity against a spectrum of microbes. However, the information regarding the mechanisms and structure-activity relationships remains elusive. The present review also focuses on the possible mechanisms of natural compounds based on their structure- activity relationships for incorporating polyphenolic compounds in the drug-development processes. Besides this work, polyphenols could reduce drug dosage and may diminish the unhidden or hidden side effects of antibiotics. Pre-clinical findings have provided strong evidence that polyphenolic compounds, individually and in combination with already approved antibiotics, work well against the development of resistance. However, more studies must focus on in vivo results, and clinical research needs to specify the importance of polyphenol-based antibacterials in clinical trials.

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.

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.

Exploring Trachyspermum ammi and Foeniculum vulgare in Hydroponic System and Compare its Chemical Constituents with Soil-Based Method: A Prospective in Agriculture.

BACKGROUND: The forthcoming problems will be of food, and soil due to environmental alteration, growing populations, pollution, and exhaustion of natural resources among other factors. Hydroponic farming has the capacity to alleviate the intimidation of these con-cerned issues in the agricultural system. Hydroponics is recommended as an alternative way to enhance product yield compared to conventional agriculture. OBJECTIVE: The present study aimed to determine the different growth parameters and constituents of soil-grown and hydroponically grown Trachyspermum ammi and Foeniculum vulgare for the first time, which could be a patentable in future. METHODS: In this study, extraction was carried out by maceration method using methanol as a solvent whereas, growth parameters were performed by the leaves number, plant height, and leaf area. Chlorophyll content was also performed in both sources. Further, a comparison of chemical constituents from different sources was analyzed by GC-MS. RESULTS: The bioactive components in hydroponically grown T. ammi were found more as compared to soil-grown T. ammi. The GC-MS analysis revealed the presence of various compounds in the methanolic extract of plant materials. CONCLUSION: Hence, hydroponics could be an alternative in agriculture and this system is now accepted globally. This method provides diverse perspectives for farmers to harvest high-yield, better quality, and enhanced bioactive compounds.