Background and purpose: Hexahydrocannabinol (HHC) is a novel psychoactive substance that has gained attention due to its psychotropic effects and temporary legal status. It is widely abused in several EU and US countries, where it serves as a legal and easily accessible alternative to ∆9-tetrahydrocannabinol (∆9-THC). Despite its widespread use, few studies have examined its in vivo effects and safety profile. Experimental approach: This study investigated the pharmacokinetics, systemic toxicity, and acute behavioural effects of HHC in male Wistar rats. A mixture of (9R)-HHC and (9S)-HHC epimers (in a 1:1 ratio) was administered via intragastric gavage at doses of 1, 5, and 10 mg/kg. Behavioural effects were assessed using the Open field test and the Prepulse inhibition of acoustic startle response. Key results: Two hours after 10 mg/kg administration, concentrations of both HHC epimers peaked in blood serum and brain tissue. According to the OECD 423 toxicity test, HHC was classified as a Category 4 substance, with an estimated lethal dose of 1000 mg/kg. Compared to the control group (administered sunflower oil), the highest dose (10 mg/kg) led to reduced locomotor activity, increased anxiety, and impaired sensorimotor gating. Conclusions & Implications: HHC readily crosses the blood-brain barrier, exhibits mild toxicity, and produces behavioural effects similar to THC-like cannabinoids.

A document by Mahmoud AL Qerom. Click on the document to view its contents.

Abstract— This research explores the integration of Convolutional Neural Networks (CNNs) into Text-to-Speech (TTS) systems to enhance emotional expressiveness in synthesized speech. Traditional TTS systems primarily focus on generating intelligible and natural-sounding speech from text but often lack the ability to convey nuanced emotional states. This limitation can reduce the effectiveness of speech applications in areas such as virtual assistants, interactive voice response systems, and assistive technologies. Our approach leverages CNNs for emotion recognition from textual data. The CNN model is trained on a dataset of emotionally labeled text, where the network learns to classify emotional content such as happiness, sadness, anger, and neutral tones. The emotional classification is then used to modulate the prosody and intonation of the speech output. By integrating the CNN with a TTS engine, we aim to produce speech that not only conveys the semantic content of the text but also reflects the intended emotional state.Introduction —    The field of speech synthesis has made significant strides in recent years, evolving from rudimentary robotic voices to highly sophisticated systems capable of producing natural-sounding speech. However, most current Text-to-Speech (TTS) systems focus primarily on the accuracy and intelligibility of the spoken content, often neglecting the emotional undertones that are crucial for effective communication. Human speech is inherently expressive, with emotion playing a vital role in conveying meaning, intent, and enhancing the listener's engagement. The lack of emotional expressiveness in synthetic speech limits its application in scenarios where conveying sentiment is essential, such as in virtual assistants, therapeutic tools, and educational platforms. This research seeks to address this gap by integrating Convolutional Neural Networks (CNNs) into TTS systems to detect and synthesize emotional speech. CNNs, known for their prowess in image and speech recognition tasks, offer a robust framework for extracting complex features from text that can be mapped to specific emotions. By analyzing the emotional context of the input text, the proposed system aims to modulate the prosodic elements of speech—such as pitch, tone, and rhythm—to produce speech that mirrors the intended emotional state.Methodology —   The methodology for developing an emotion-aware Text-to-Speech (TTS) system using Convolutional Neural Networks (CNNs) involves several key stages, each aimed at enhancing the expressiveness and emotional relevance of synthesized speech. This approach ensures that the final system can accurately detect and convey emotions in spoken language, thereby improving user engagement and satisfaction.Data Collection  — The process begins with data collection, where a diverse dataset of text and corresponding emotional labels is gathered. This dataset is crucial for training the emotion detection model and includes textual data from various sources, such as dialogues, literature, and social media. The texts are annotated with emotion labels, either manually by human annotators or by utilizing existing emotion-labeled datasets. This foundational step provides the necessary data for the subsequent stages of the system's development.Text Preprocessing  —  Following data collection, the text preprocessing stage prepares the textual data for analysis. This involves text normalization, where the text is standardized by converting it to lowercase, removing punctuation, and correcting spelling errors. The text is then tokenized, breaking it down into individual words or sub-words, which serve as the basic units for further analysis. These tokens are vectorized into numerical representations using methods like Word2Vec, GloVe, or BERT embeddings. This preprocessing ensures that the text data is in a suitable format for emotion detection.Emotion Detection  — The heart of the system lies in the emotion detection process, which employs Convolutional Neural Networks (CNNs) to classify the emotional tone of the input text. A CNN architecture is designed specifically for text classification, featuring convolutional and pooling layers that extract and analyze features from the text. The model is trained on the preprocessed text data, learning to associate specific patterns in the text with particular emotions. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring that it can reliably detect emotions from new text inputs.Text-to-Speech (TTS) Engine  — The next stage involves the Text-to-Speech (TTS) engine, which synthesizes speech from the text, incorporating the emotional adjustments made in the previous stage. The TTS engine converts the processed text into its phonetic form, applies the prosody adjustments based on the detected emotions, and generates the final speech waveform. Advanced models like Tacotron or WaveNet may be used for this synthesis, providing high-quality, natural-sounding speech that aligns with the emotional content of the text.After developing the core components, system integration is performed to create a cohesive system. This includes developing an API that connects the user interface, where text input is provided, with the backend models responsible for preprocessing, emotion detection, and speech synthesis. The system is optimized for real-time processing to ensure that text can be input and speech can be generated with minimal delay. Extensive testing and validation are conducted to ensure the system functions correctly, produces accurate emotional speech, and meets user expectations for quality.

The strategies that policymakers take to mitigate climate change will have considerable implications for human exposure to air quality, with air quality co-benefits anticipated from climate change mitigation. Few studies try to model these co-benefits at a regional scale and even fewer consider health inequalities in their analyses.   We analyse the health impacts across Western and Central Europe from exposure to fine particulate matter (PM2.5) and surface level ozone (O3) in 2014 and in 2050 using three scenarios with different levels of climate change mitigation, using a high-resolution atmospheric chemistry model to simulate future air quality. We use recent health functions to estimate mortality related to the aforementioned pollutants. We also analyse the relationship between air quality mortality rate per 100,000 people and Human Development Index to establish if reductions in air quality mortality are achieved equitably.   We find that air quality-related mortality (PM2.5 + O3 mortality) will only reduce in the future following a high-mitigation scenario (54%). It could increase by 7.5\% following a medium-mitigation scenario and by 8.3% following a weak mitigation scenario. The differences are driven by larger reductions in PM2.5-related mortality and a small reduction in O3-related mortality following the sustainable scenario, whereas for the other scenarios, smaller improvements in PM2.5-related mortality are masked by worsening O$_3$-related mortality.   We find that less developed regions of European countries have higher mortality rates from PM2.5 and O3 exposure in the present day, but that this inequity is reduced following greater climate change mitigation.

The study aims to measure the performance of Indian states/Union territories (UTs) in response to COVID-19.It employs a hyperbolic distance function approach based data envelopment model to evaluate the relative performance of the states/ UTs. The result shows that Maharashtra and Arunachal Pradesh are top performers, followed by Andhra Pradesh and Karnataka. Moreover, Chandigarh, Dadra and Nagar Haveli, Puducherry and Tripura signify high inefficiency among states and UTs. In addition, it signifies that the states/UTs with high vulnerability have achieved better efficiency. The results hold practical implications for policymakers in India regarding the preparedness and management of future pandemics.

Instance-level human parsing, critical for human-centric analysis, involves labeling pixels of human body parts and associating them with specific instances. Despite progress in multi-person parsing, segmenting individuals in dense crowds remains challenging. The Visual Saliency-Based Human Parsing (ViS-HuP) algorithm addresses this by using visual saliency to enhance body pixel clarity and incorporating edge detection to refine instance boundaries within a multi-task learning framework. Tested on the Crowd Instance-level Human Parsing (CIHP) dataset[1], ViS-HuP outperforms conventional methods, showing significant accuracy improvements in crowded scenes.

In today's society, women are increasingly vulnerable to sexual violence, whether at home, in the workplace, or in public spaces. This study aims to explore the psychological consequences of sexual violence among women aged 20-30 in northern India, with a focus on fear, stress, and anxiety. A qualitative approach was employed, using semi-structured interviews with a purposive sample of 20 women. Data were collected through text-based online interviews, and thematic analysis was performed to identify key patterns and insights related to the psychological impact of sexual violence. The findings revealed that the fear of sexual violence led to heightened stress and anxiety, significantly affecting participants' mental well-being. Many women reported behavioural changes such as avoiding certain locations and altering daily routines to stay safe. The study also highlighted the role of societal norms and family pressures in shaping how women deal with these threats. This study highlights the urgent need for mental health support for women coping with the psychological toll of sexual violence. It also calls for societal and policy-level interventions to create safer environments and challenge the patriarchal structures that perpetuate fear and restrict women's freedom.

Greenhouse gases contribute to atmospheric warming by trapping heat that reaches the Earth's surface from the sun. Tractors running on fossil fuels emit greenhouse gases, which harm the environment. Thus, reducing CO2 emissions from tractor use in agriculture is essential to mitigate environmental issues. Land consolidation merges small and fragmented agricultural lands to create larger and more efficient farming plots. This study examines the impact of farm size, number of parcels, parcel aspect ratio, and parcel distance on CO2 emissions from tractor use in agricultural areas. Various scenarios were created and grouped for all parameters. Land consolidation redesigns agricultural holdings by considering factors such as the number of parcels, their shape, and their size ratios. Results showed that the parcel aspect ratio's effect on CO2 emissions was not statistically significant. In almost all medium and large farm size scenarios, high emission values were not observed. The study concluded that planning small farm parcels as a single piece close to the farm center and limiting medium and large farms to a maximum of 4-5 parcels significantly reduces emission values. Thus, effective land consolidation planning can substantially lower CO2 emissions from tractor use in agriculture.

Agave spirits have gained global recognition and hold a central position within the cultural heritage of Mexico. Traditional distilleries characterized by open fermentations driven by local microbial communities, persist despite the rise of industrial-scale counterparts. In this review, we explore the environmental conditions and production practices that make the must of cooked agave stems a unique habitat for colonizing microorganisms. Additionally, we review selected studies that have characterized yeast species within these communities, with a focus on their metabolic traits and genomic features. Over fifty fungal species, predominantly Saccharomycetales and few Basidiomycetes, along with a similar number of lactic and acetic acid bacteria, have been identified. Despite variations in the chemical composition of the agave substrate and diversity of cultural practices associated with each traditional fermentation process, yeast species such as Saccharomyces cerevisiae, Kluyveromyces marxianus, Torulaspora delbrueckii, and several Pichia species have been consistently isolated across all agave spirit-producing regions. Importantly, cooked agave must is rich in fermentable sugars, yet it also contains inhibitory compounds that influence the proliferation dynamics of the microbial community. We discuss some of the genetic traits that may enable yeasts to flourish in this challenging environment and how human practices may shape microbial diversity by promoting the selection of microbes that are well-adapted to agave fermentation environments. The increasing demand for agave spirits, combined with concerns about the preservation of natural resources and cultural practices associated with their production, underscores the need to deepen our understanding of all key players, including the yeast communities involved.

The rapid expansion of artificial intelligence applications in conversational systems has introduced new challenges in anticipating user needs and generating responses that are contextually appropriate across multiple turns of dialogue. A novel approach was introduced to address these challenges, incorporating rehearsed prompt generation combined with perspective-taking strategies to enable AI models to anticipate follow-up questions before they are posed. Through systematic prompt rehearsals, GPT-Neo was trained to simulate various user viewpoints and generate more adaptive responses, thereby improving the model's capacity to manage complex, multi-turn interactions. The experimental results revealed substantial improvements in coherence, relevance, and diversity, particularly when rehearsed prompts were combined with perspective-taking techniques. Additionally, automated evaluation metrics confirmed the model's enhanced performance across multiple domains, suggesting significant applications in customer service, educational tutoring, and research assistance. These findings reaffirm the practical value of anticipatory question answering in AI systems, offering a scalable and efficient solution for improving user experience in dynamic environments.

Physical activity improves cognitive function and reduces depression by enhancing neuroplasticity, increasing mood-enhancing neurochemicals, lowering cortisol levels, and elevating brain-derived neurotrophic factor (BDNF) and endocannabinoid levels. Lifestyle changes such as aromatherapy, dietary adjustments, massage therapy, and mindfulness meditation are also significant. Aromatherapy alleviates stress and anxiety through its effects on the limbic system. Anti-inflammatory diets like the Mediterranean diet and micronutrients (e.g., B vitamins, magnesium, zinc) support brain function and reduce inflammation. Massage therapy promotes relaxation and mood improvement, while mindfulness meditation and pet therapy aid emotional regulation. Sleep hygiene and strong social support are integral to managing mood disorders. Non-pharmacological interventions, including brain stimulation therapies and psychotherapy, offer additional options. Electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) are effective for severe and treatment-resistant depression. Eye movement desensitization and reprocessing (EMDR) addresses trauma-related depression, while phototherapy treats seasonal affective disorder (SAD) by regulating circadian rhythms. Cognitive behavioral therapy (CBT) and interpersonal therapy (IPT) target negative thought patterns and enhance social relationships. Optimal integration into clinical practice requires assessing patient needs, personalizing interventions, and multidisciplinary collaboration. Addressing patient compliance, socioeconomic barriers, cultural sensitivity, and co-occurring conditions is essential. Future research will emphasize technology-enhanced interventions and genetic studies to refine treatments and improve outcomes. This review emphasizes the role of physical activity and lifestyle modifications in preventing, managing, and treating major depressive disorder (MDD).

Empowering the Future of Evidence-Based Healthcare: The Cochrane Early Career Professionals Network

St. John’s wort (SJW, Hypericum perforatum ) is noted for its potential in treating mild to moderate depression, attributed to its active compounds, hypericin and hyperforin. These compounds boost neurotransmitter levels—serotonin, norepinephrine, and dopamine—enhancing mood regulation and symptom relief. Also, SJW has anti-inflammatory and neuroprotective effects. Clinical evidence supports its efficacy for mild to moderate depression, showing comparable results to selective serotonin reuptake inhibitors (SSRIs), though its effectiveness in severe depression is less certain. SJW has common side effects like gastrointestinal issues and photosensitivity and poses serious risks due to interactions with medications, which can reduce the efficacy of drugs such as anticoagulants and hormonal contraceptives and lead to serotonin syndrome. Globally, it is classified as an herbal medicine or dietary supplement, with varying regulatory status and quality control issues. Ethical concerns include its use in vulnerable populations and the need for informed consent due to variable quality and interactions. In clinical practice, SJW is utilized for mild to moderate depression, either as an adjunct to conventional treatments or as a standalone therapy. Standard treatments for psychological disorders usually include pharmacotherapy, brain stimulation, and psychotherapy. However, many patients opt for alternative treatments like nutraceuticals or dietary supplements due to drugs’ side effects, dependency, withdrawal symptoms, and the high costs of conventional options. Factors such as lack of insurance, social stigma, non-adherence, and dissatisfaction with traditional treatments often drive this preference. This review evaluates its use in major depressive disorder (MDD), focusing on long-term efficacy, safety, and potential personalized medicine approaches. Innovations in combination therapies, delivery methods, and biomarkers may enhance therapeutic outcomes. SJW and complementary therapies offer additional options, particularly for patients resistant to conventional treatments, given that standard antidepressants may not address all underlying factors of MDDs, such as inflammation and nutritional deficiencies.

 Note:  Telecommunication theories play a special role in the modeling, analysis and design of molecular nanocommunication systems.A nano network is a nano scale communication network between nano devices.  Nano devices  face special challenges in performance  due to limitations in power management processing ability .  Therefore, these devices are expected to perform simple tasks that require different and new approaches. In the molecular communication system, the transmitter sends information through chemical molecules called information molecules, and after it is released in the environment, it is received and decoded by the communication receiver.

Note: When the liquid passes through coils of single-walled carbon nanotubes, an electric voltage is created. This technique   is used to make liquid flow sensors to detect very small amounts of liquids and also to generate voltage in nano biosensors applications  . Also, conductive nanotubes with high ionic strength produce more voltage  .Nanotubes have a very high surface density, which makes the nanotubes strong. It can be said that this property appears due to their remarkable smallness.

DNA cytosine deaminase APOBEC3B (A3B) is one of the endogenous sources of somatic mutations in many types of human cancers and is associated with tumor progression rather than tumorigenesis. However, it remains uncertain whether APOBEC3B-induced mutations accelerate tumor progression or not. In this paper, we established a mouse model with A3B overexpression and investigated whether the introduction of A3B overexpression accelerates tumor development in Tp53 hemizygous mice. A3B expression was validated by qPCR, immunoblotting, and immunohistochemistry in mouse tissues, and in vitro CDA assays revealed that A3B has its CDA activity in mouse tissues. However, we did not observe any difference in tumor development between the mice with or without A3B expression. A3B expression and its CDA activity were confirmed in tumor tissues of mice overexpressing A3B. Therefore, we concluded that the introduction of A3B overexpression did not accelerate tumor development in Tp53 hemizygous mice. Our mouse model with A3B overexpression is well-validated and useful for further research.

Background: Thymoma is often accompanied by myasthenia gravis (MG), and the resection of thymoma improves myasthenic symptoms in patients with thymoma and MG (TMG), but some patients still have no relief. Through proteomic analysis, we examined preoperative serum samples from patients with TMG to identify key prognostic proteins that could serve as a foundation for clinically predicting postoperative efficacy and guiding treatment selection. Method: In this study, 20 patients with TMG meeting the inclusion criteria were selected and divided into an effective group (T1), an ineffective group (T2) with 10 cases each, and a healthy control group (C) with 9 cases. Blood samples from the three groups were collected through Data independent acquisition (DIA) proteomic analysis performed by mass spectrometry to identify differential proteins expressed and search for key proteins associated with myasthenia prognosis. Finally, the target proteins were validated through the utilization of Enzyme-Linked ImmunoSorbent Assay (ELISA). Results: 514 proteins were identified in this research. Between the T1 and T2 groups, there were 20 proteins that exhibited differential expression, with 10 showing up-regulation and 10 displaying down-regulation. KEGG functional annotation indicated that these proteins were mainly involved in signaling pathways such as complement and coagulation cascade, prion disease, systemic lupus erythematosus, neutrophil extracellular trap formation, and transcription dysregulation in cancer. 3 proteins were discovered to have a significant correlation with the prognosis of TMG, L-selectin (SELL) was down-regulated, HLA class I histocompatibility antigen (HLA-A) and Complement 5 (C5) were up-regulated. ELISA results confirmed the proteomic results. Conclusion: HLA-A, C5 and SELL may be potential prognostic biomarkers of TMG.

Animal stoichiometry influences critical processes from organismal physiology to biogeochemical cycles. However, it remains uncertain whether animal stoichiometry follows predictable scaling relationships with body mass and whether adaptation to terrestrial or aquatic environments constrains elemental allocation. We tested both interspecific and intraspecific body-mass scaling relationships for nitrogen (N), phosphorus (P), and N:P content using a subset of the StoichLife database, which includes 9,933 individual animals across 1,543 species spanning 10 orders of magnitude in body mass from terrestrial, freshwater, and marine realms. Our results show that body mass predicts intraspecific stoichiometric variation, accounting for 42-45% of the variation in 27% of vertebrate and 35% of invertebrate species. However, body mass was less effective at explaining interspecific variation, with taxonomic identity emerging as a more significant factor. Differences between aquatic and terrestrial organisms were observed only in invertebrate interspecific %N, suggesting that realm has a relatively minor influence on elemental allocation. Our study, based on the most comprehensive animal stoichiometry database to date, revealed that while body mass is a good predictor of intraspecific elemental content, it is less effective for interspecific patterns. This highlights the importance of evolutionary history and taxonomic identity over general scaling laws in explaining stoichiometric variation.

Background: Hypoglycemia is a rarely reported complication of Asparaginase (ASP) therapy in children with lymphoblastic leukemia/lymphoma (ALL/LLy). We sought to identify risk factors and outcomes among patients with ASP-induced hypoglycemia (AIH) at our institution. Methods: Retrospective cohort study using electronic medical records to identify all patients who received ASP and had diagnosis of hypoglycemia between 6/1/2017-6/30/2022. Demographic and clinically relevant data were collected. Results: A total of 672 patients received ASP, with 8% having AIH–defined by a measured low blood glucose level within 14 days of ASP administration and other causes of hypoglycemia excluded. Median age at ALL/LLy diagnosis was 4.4 years (Interquartile range [IQR]: 2.5 – 7.7) which was younger than patients without AIH (median 6.9 years, p-value 0.005), and median BMI z-score 0.50 (IQR: -0.46 – 0.95). Initial hypoglycemia event was during Induction therapy in 71%, with median time from ASP to hypoglycemia diagnosis of 11 days (IQR: 6-15). Median duration of the hypoglycemia episode was 11 days (IQR: 7-19). Recurrent hypoglycemia with subsequent ASP doses occurred in 84% of patients, with a median duration 14 days (IQR: 8-21). Overall survival of the AIH cohort was 80% (85% if limited to patients with newly diagnosed ALL/LLy), with 3 years’ median follow up. In univariate analysis, hypoglycemia severity was not associated with age, sex, ethnicity, or weight. Conclusion: AIH is relatively common with no clear risk factors besides younger age. It can recur and become more severe with longer duration. AIH screening and management should be implemented.

In this paper, a relay life prediction method based on service performance degradation parameters is proposed for the performance degradation of relays during long-term operation. Firstly, the soft threshold method is selected for wavelet noise reduction to process the relay performance data and remove the noise interference. Then, principal component analysis is used to extract the health factors affecting relay life from the original data, and finally the first principal component score is taken as the relay health factor and smoothed with the sliding average method. Finally, the extracted health factors are utilized in the LSTM-based relay life prediction model to predict the relay life, and the performance of the LSTM model is compared with that of the ARIMA model in relay life prediction, and the experimental results show that the LSTM model has better prediction performance. Meanwhile, compared with the traditional life prediction method, this method is more scientific and reliable, which helps to improve the accuracy of relay life prediction and reduce the operation and maintenance cost.

Aim：This study aims to describe the current global research status, identify the most influential countries, research institutions, researchers and research hotspots through conducting bibliometric analysis on literature related to the correlation between gut microbiota and tumor immune microenvironment (TIME) from January 1, 2014, to May 28, 2024, in order to provide insights for future research and development trends. Methods：We searched for all literature related to gut microbiota and TIME published from January 1, 2014, to May 28, 2024, in the Web of Science Core Collection database. Then, we further conducted bibliometric analysis and created visual maps of the published literature on countries, institutions, authors, keywords, references, etc. by using CiteSpace (6.2R6), VOSviewer (1.6.20), and bibliometrics (based on R 4.3.2). Results：491 documents were ultimately included, with a rapid increase in the number of publications starting from 2019. The country with the highest number of publications is China, followed by the United States. And Germany has the highest number of citations of literature. From the centrality perspective, the United States has the highest influence in this field. The institution with the most publications is Shanghai Jiao Tong University in China. However, the institution with the most citations is the NIH National Cancer Institute (NCI) in the United States. As for the author, Professor Giorgio Trinchieri from the National Institutes of Health has produced the most results in this field. And the most citations are FAN XZ. The results of journal publications show that the top three journals with the highest number of published papers are Frontiers in Immunology, Cancers, and Frontiers in Oncology. The top three most frequently occurring keywords are gut microbiota, tumor microenvironment, and immunotherapy. Conclusions: This study systematically elaborates on the research progress related to gut microbiota and TIME over the decade year. Research results indicate that the number of publications has rapidly increased since 2019, with research hotspots including ”gut microbiota”, ”tumor microenvironment” and ”immunotherapy”. Exploring specific gut microbiota or derived metabolites on the behavior of immune cells in the TIME, regulating the secretion of immune molecules, influencing immunotherapy are research hotspots and future research directions.

Lymphoma in horses is uncommon and rarely diagnosed as the cause of ataxia in horses. This case report describes a two-year-old Standardbred cryptorchid colt who presented with acute onset of grade 3.5/5 ataxia. Due to the severity of the ataxia, a full neurologic examination could not be completed, and the colt was humanely euthanized. Post-mortem examination revealed multiple osteochondritis dissecans lesions in the cervical and lumbar spine. Initially this was thought to be the cause of the ataxia, however histopathology revealed multifocal areas where the spinal nerve roots and epidural surface of the dural mater of the meninges were surrounded or covered by plaques of neoplastic round cells which infiltrated the adipose tissue surrounding the spinal tissues. Atypical round cells similar to those observed in the spinal meningeal tissues were also found in the kidneys, lungs, liver, testes and one lymph node. Immunohistochemical staining of meningeal tissue samples revealed moderate to intense cytoplasmic/membranous immunoreactivity for CD20 and a diagnosis of multicentric diffuse large B cell lymphoma was made. Lymphoma should be included in the differential diagnoses for a horse presenting with ataxia and neurologic deficits

The study of diet is central to wildlife ecology, management and conservation. Metabarcoding increased the capability to identify species contributing to wildlife diet and blocking primers can maximize the detection of prey. Relative read abundance (RRA) of different prey species has been used as semi-quantitative approach, assuming that RRA reflects species contribution to diet. However, this approach has been contested and it is unclear how blocking primers might affect the result. We tested accuracy of RRA to estimate diet by feeding captive wolves six different diets. We analyzed samples without and with four blocking primer concentrations (5, 10, 15 and 20x) to provide insight into the validity of the semi-quantitative metabarcoding approach and the influence of blocking primer in results. RRA produced a highly accurate representation of actual contribution to diet, that was best without blocking primer (0.775 ± 0.033; P <0.001; R2=0.815) with no difference in the number of diet items detected when compared with analyses with blocking primer. Adding blocking primer resulted in higher proportions of reads of diet items, as opposed to wolf sequences, but did not increase the probability of detecting diet components, increased detections of items not fed to wolves, and produced slightly less accurate estimates of diet composition. Finally, resampling suggested that sample sizes beyond 30 scats reduced the variation in results. While our results are promising and support the use of metabarcoding to determine volumetric contribution of items to diet, caution and further research are needed before safe extrapolation to filed studies.

Objective: To study the effect of Yoga in pregnancy on maternal depression using the Edinburgh Perinatal Depression Scale. Design: A Randomised Control Study from December 2020 to September 2022. Setting: Tertiary care (Referral centre) Sample: 260 pregnant patients were screened, 32 declined to participate in the study and 28 were excluded based on the exclusion criteria. The remaining 200 women, matched for age, weight, parity and physical activity were randomised into two groups: Group I (n = 100, undergoing Yoga therapy) and Group II (n = 100, given usual antenatal care). There was no loss to follow up or any adverse effect seen in either group. Methods: A trained instructor provided two physical sessions of Yoga, each lasting for 60 minutes and further online sessions for five days a week for 3 months. Main outcome measure: Edinburgh Perinatal Depression Scale (EPDS) questionnaire was assessed as the primary outcome at recruitment, 32 weeks (Antenatal), 1 week and 6 week post-partum in both the groups. Results: In the 200 women randomised and matched for age, weight, parity and physical activity there was no complication seen throughout the pregnancy and no patient was lost to follow up in either group. The majority of patients exhibited a decline in the EPDS score in Group 1 (45%) compared to Group 2 in which the majority had same score (64%). The mean difference of scores between recruitment and 6 week post-partum was statistically significant [p value = <0.05]. Conclusion: Yoga in pregnancy significantly decreases maternal depression in an easy manner with no proven adverse effects.