The changing epidemiology of lumpy skin disease in Russia since the first introduction from 2015 to 2020.

Lumpy skin disease (LSD) is an economically important transboundary disease affecting cattle, causing large economic losses such as decreased production and trade restrictions. LSD has been a historically neglected disease since it previously caused disease limited to the African continent. Currently, the epidemiology of LSD virus is based on how the disease is transmitted in tropical and sub-tropical climates. The understanding of its epidemiology in hemiboreal climates is not well understood and needs urgent attention to expand the current knowledge. In this study, the epidemiological findings on LSD in Russia over a 6-year period are summarized and discussed. A total of 471 outbreaks were identified spanning over a 9000 km range. The outbreaks of LSD occur primarily in small holder farms (backyard) compared to commercial farms between mid-May through mid-November including weather conditions with snow and freezing temperatures that preclude vector activity. Mortality and morbidity varied across the 6 years ranging from 1.19% to 61.8% and 0% to 50%, respectively, with a tendency to decline from 2015 to 2020. The geographic pattern of spread was assessed by means of directionality, indicating a northward movement from 2015 to 2016, with a consequent East turn in 2017 through Siberia to the Far East by 2020. All cases occurred along the border with Kazakhstan. Mathematical modelling showed that the disease tended to form statistically verified annual spatiotemporal clusters in 2016-2018, whereas in 2019 and 2020 such segregation was not evident. The trend of spread was mainly either from south to north or from south to a north-east direction.

Spatiotemporal Mapping of Online Interest in Cannabis and Popular Psychedelics before and during the COVID-19 Pandemic in Poland.

BACKGROUND: Psychedelics represent a unique subset of psychoactive substances that can induce an aberrant state of consciousness principally via the neuronal 5-HT2A receptor. There is limited knowledge concerning the interest in these chemicals in Poland and how they changed during the pandemic. Nonetheless, these interests can be surveyed indirectly via the web. OBJECTIVES: We aim to conduct a spatial-temporal mapping of online information-seeking behavior concerning cannabis and the most popular psychedelics before and during the pandemic. METHODS: We retrieved online information search data via Google Trends concerning twenty of the most popular psychedelics from 1 January 2017 to 1 January 2022 in Poland. We conducted Holt-Winters exponential smoothing for time series analysis to infer potential seasonality. We utilized hierarchical clustering analysis based on Ward's method to find similarities of psychedelics' interest within Poland's voivodships before and during the pandemic. RESULTS: Twelve (60%) psychedelics had significant seasonality; we proved that psilocybin and ayahuasca had annual seasonality (p-value = 0.0120 and p = 0.0003, respectively), and four substances-LSD, AL-LAD, DXM, and DOB-exhibited a half-yearly seasonality, while six psychedelics had a quarterly seasonal pattern, including cannabis, dronabinol, ergine, NBOMe, phencyclidine, and salvinorin A. Further, the pandemic influenced a significant positive change in the trends for three substances, including psilocybin, ergine, and DXM. CONCLUSIONS: Different seasonal patterns exist for psychedelics, and some might correlate with school breaks or holidays in Poland. The pandemic induced some changes in the temporal and spatial trends. The spatial-temporal trends could be valuable information to health authorities and policymakers responsible for monitoring and preventing addictions.

Psychedelics: Alternative and Potential Therapeutic Options for Treating Mood and Anxiety Disorders.

The word "psychedelic" (psyche (i.e., the mind or soul) and delos (i.e., to show)) has Greek origin and was first coined by psychiatrist Humphry Osmond in 1956, who had been conducting research on lysergic acid diethylamide (LSD) at the time. Psychedelic drugs such as N,N-DMT/DMT (N,N-dimethyltryptamine), 5-MeO-DMT (5-methoxy-N,N-dimethyltryptamine), LSD (lysergic acid diethylamide), MDMA (3,4-methylenedioxymethamphetamine) and psilocybin have had significant value as an entheogen in spiritual, religious (shamanic) and sociocultural rituals in Central and South American cultures for thousands of years. In the 1960s, the globalization of these drugs and their subsequent spread outside of their indigenous, old-world cultures, led to the subsequent implementation of strict drug control laws in many Western countries. Even today, psychedelics are still classified as Schedule I drugs, resulting in a still lingering negative stigmatization/perception, vilification, and ultimate criminalization of psychedelics. This controversy still lingers and still limits scientific research and full medical acceptance. For many years up until recently, the spiritual, religious and medicinal value of these drugs could not be explored in a scientific context. More recently, a second wave of psychedelic research is now focusing on psychedelics as neuropharmaceuticals to treat alcohol and tobacco addiction, general mood and anxiety disorders and cancer-related depression. There is now a vast array of promising evidence-based data to confirm the years of anecdotal evidence of the medicinal values of psychedelics. Natural therapeutic alternatives such as psychedelic drugs may provide a safe and efficacious alternate to conventional drugs used to treat mood and anxiety disorders. In a Western context in particular, psychedelic drugs as therapeutic agents for mood and anxiety disorders are becoming increasingly of interest amidst increasing rates of such disorders globally, changing social constructions, the implementation of government regulations and increasing investment opportunities, that ultimately allow for the scientific study to generate evidenced-based data. Alternative psychotherapeutic interventions are gaining interest also, because of their low physiological toxicity, relatively low abuse potential, safe psychological effects, and no associated persisting adverse physiological or psychological effects during and after use. On the other hand, conventional psychotic drugs and anti-depressants are becoming less favorable because of their adverse side effects. Psychedelic neuropharmaceutical interventions may with medical oversight be the solution to conventional psychiatric disorders such as depression and anxiety, and an alternative to conventional psychiatric treatment options. This paper will review the therapeutic potential of psychedelic drugs as alternative therapeutic options for mood and anxiety disorders in a controlled, clinical setting, where the chances of adverse psychological episodes occurring are mitigated.

DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images.

BACKGROUND AND OBJECTIVE: Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 from community-acquired pneumonia (CAP) is urgently needed. However, the spatial uncertainty and morphological diversity of COVID-19 lesions in the lungs, and subtle differences with respect to CAP, make differential diagnosis non-trivial. METHODS: We propose a deep represented multiple instance learning (DR-MIL) method to fulfill this task. A 3D volumetric CT scan of one patient is treated as one bag and ten CT slices are selected as the initial instances. For each instance, deep features are extracted from the pre-trained ResNet-50 with fine-tuning and represented as one deep represented instance score (DRIS). Each bag with a DRIS for each initial instance is then input into a citation k-nearest neighbor search to generate the final prediction. A total of 141 COVID-19 and 100 CAP CT scans were used. The performance of DR-MIL is compared with other potential strategies and state-of-the-art models. RESULTS: DR-MIL displayed an accuracy of 95% and an area under curve of 0.943, which were superior to those observed for comparable methods. COVID-19 and CAP exhibited significant differences in both the DRIS and the spatial pattern of lesions (p<0.001). As a means of content-based image retrieval, DR-MIL can identify images used as key instances, references, and citers for visual interpretation. CONCLUSIONS: DR-MIL can effectively represent the deep characteristics of COVID-19 lesions in CT images and accurately distinguish COVID-19 from CAP in a weakly supervised manner. The resulting DRIS is a useful supplement to visual interpretation of the spatial pattern of lesions when screening for COVID-19.