this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr).Monkeypox, a zoonotic infection caused by an orthopoxvirus, is endemic in parts of Africa. On August 4, 2022, the U.S. Department of Health and Human Services declared the U.S. monkeypox outbreak, which began on May 17, to be a public health emergency (1,2). After detection of the first U.S. monkeypox case), CDC and health departments implemented enhanced monkeypox case detection and reporting. Among 2,891 cases reported in the United States through July 22 by 43 states, Puerto Rico, and the District of Columbia (DC), CDC received case report forms for 1,195 (41%) cases by July 27. Among these, 99% of cases were among men; among men with available information, 94% reported male-to-male sexual or close intimate contact during the 3 weeks before symptom onset. Among the 88% of cases with available data, 41% were among non-Hispanic White (White) persons, 28% among Hispanic or Latino (Hispanic) persons, and 26% among non-Hispanic Black or African American (Black) persons. Forty-two percent of persons with monkeypox with available data did not report the typical prodrome as their first symptom, and 46% reported one or more genital lesions during their illness; 41% had HIV infection. Data suggest that widespread community transmission of monkeypox has disproportionately affected gay, bisexual, and other men who have sex with men and racial and ethnic minority groups. Compared with historical reports of monkeypox in areas with endemic disease, currently reported outbreak-associated cases are less likely to have a prodrome and more likely to have genital involvement. CDC and other federal, state, and local agencies have implemented response efforts to expand testing, treatment, and vaccination. Public health efforts should prioritize gay, bisexual, and other men who have sex with men, who are currently disproportionately affected, for prevention and testing, while addressing equity, minimizing stigma, and maintaining vigilance for transmission in other populations. Clinicians should test patients with rash consistent with

Recent findings indicate that pedunculopontine tegmental nucleus (PPTg) neurons encode reward-related information that is context-dependent. This information is critical for behavioral flexibility when reward outcomes change signaling a shift in response patterns should occur. The present experiment investigated whether NMDA lesions of the PPTg affects the acquisition and/or reversal learning of a spatial discrimination using probabilistic reinforcement. Male Long-Evans rats received a bilateral infusion of NMDA (30 nmoles/side) or saline into the PPTg. Subsequently, rats were tested in a spatial discrimination test using a probabilistic learning procedure. One spatial location was rewarded with an 80% probability and the other spatial location rewarded with a 20% probability. After reaching acquisition criterion of 10 consecutive correct trials, the spatial location – reward contingencies were reversed in the following test session. Bilateral and unilateral PPTg-lesioned rats acquired the spatial discrimination test comparable to that as sham controls. In contrast, bilateral PPTg lesions, but not unilateral PPTg lesions, impaired reversal learning. The reversal learning deficit occurred because of increased regressions to the previously ‘correct’ spatial location after initially selecting the new, ‘correct’ choice. PPTg lesions also reduced the frequency of win-stay behavior early in the reversal learning session, but did not modify the frequency of lose-shift behavior during reversal learning. The present results suggest that the PPTg contributes to behavioral flexibility under conditions in which outcomes are uncertain, e.g. probabilistic reinforcement, by facilitating sensitivity to positive reward outcomes that allows the reliable execution of a new choice pattern.

The aim of study this to enhance understanding of employee’s attitude towards organizational politics. The positive and negative impact of perception of organizational politics on employee job attitudes is discussed in this paper which caters to one of the most significant issue attracting much of attention by organizational scientists. Numerous productive and counter productive work attitudes are identified by extensive literature review of research papers, articles and different sources at internet. An extensive study of literature has been carried out to discuss two theoretical models of perception of politics. Therein, the article sheds light on the positive outcomes of politics through the first model followed by underlining the negative outcomes of politics at the workplace. The paper also enlightens readers` knowledge and understanding on how organization can work to make the most of this prospect whilst ensuring it does not affect any organizational objectives. The review also forwards lays discussion on both the models for scholars enthusiastic to test and confirm the assertions of both the models for better managerial implication in future.

Background Skeletal muscle atrophy is a debilitating complication of many chronic diseases, disuse conditions, and ageing. Genome‐wide gene expression analyses have identified that elevated levels of microRNAs encoded by the H19X locus are among the most significant changes in skeletal muscles in a wide scope of human cachectic conditions. We have previously reported that the H19X locus is important for the establishment of striated muscle fate during embryogenesis. However, the role of H19X‐encoded microRNAs in regulating skeletal mass in adults is unknown. Methods We have created a transgenic mouse strain in which ectopic expression of miR‐322/miR‐503 is driven by the skeletal muscle‐specific muscle creatine kinase promoter. We also used an H19X mutant mouse strain in which transcription from the locus is interrupted by a gene trap. Animal phenotypes were analysed by standard histological methods. Underlying mechanisms were explored by using transcriptome profiling and validated in the two animal models and cultured myotubes. Results Our results demonstrate that the levels of H19X microRNAs are inversely related to postnatal skeletal muscle growth. Targeted overexpression of miR‐322/miR‐503 impeded skeletal muscle growth. The weight of gastrocnemius muscles of transgenic mice was only 54.5% of the counterparts of wild‐type littermates. By contrast, interruption of transcription from the H19X locus stimulates postnatal muscle growth by 14.4–14.9% and attenuates the loss of skeletal muscle mass in response to starvation by 12.8–21.0%. Impeded muscle growth was not caused by impaired IGF1/AKT/mTOR signalling or a hyperactive ubiquitin–proteasome system, instead accompanied by markedly dropped abundance of translation initiation factors in transgenic mice. miR‐322/miR‐503 directly targets eIF4E, eIF4G1, eIF4B, eIF2B5, and eIF3M. Conclusions Our study illustrates a novel pathway wherein H19X microRNAs regulate skeletal muscle growth and atrophy through regulating the abundance of translation initiation factors, thereby protein synthesis. The study highlights how translation initiation factors lie at the crux of multiple signalling pathways that control skeletal muscle mass.

Introduction: Predicting outcome after mechanical thrombectomy (MT) for ischemic stroke due to LVO can inform prognosis and guide early management. Prior studies report heterogeneity in risk factors for poor outcome. Machine learning may identify patterns of poor outcome from diverse variables that are difficult to discern with conventional statistical methods. Methods: Using a retrospective database of 233 stroke patients (2015-20) who had MT for LVO, we created machine learning predictive models with clinical and imaging variables for the following 4 outcomes: decompressive craniectomy, discharge mRS ≥4, development of post-stroke cerebral edema with mass effect, and in-hospital mortality. We compared 10 learner models: AdaBoost, Tree, Random Forest, Neural Network, CN2 Rule Induction, Logistic Regression, Naïve Bayes, kNN, Stochastic Gradient Descent, and Support Vector Machine. Variables were ranked by 5 scoring methods: information gain, information gain ratio, gini decrease, chi-square, ReliefF, and fast correlation-based filter. A prediction model was created using the top 5 variables to maximize the area under the receiver operating characteristic curve and classification accuracy. Models were 5-fold cross validated. Analyses were conducted via Stata and Orange Data Mining. Results: Prediction model sets of 5 variables were generated for the 4 outcomes of interest. Infarction volume was most important for predicting decompressive craniectomy, discharge mRS ≥4, and in-hospital mortality. Cerebral edema was important for decompressive craniectomy, discharge mRS ≥4, and in-hospital mortality. Initial NIHSS was important for decompressive craniectomy, discharge mRS ≥4, and in-hospital mortality. Contrast staining on post-procedural CT was important for cerebral edema (χ 2 11.9) and in-hospital mortality (χ 2 21.8). Patient age was important for discharge mRS ≥4 and decompressive craniectomy. Conclusion: We identified prediction models consistent with established prognostic variables. Post-MT contrast staining is a novel and important predictor of poor outcome, which merits further research. In conclusion, machine learning can be used to create accurate prediction models for outcome after MT for ischemic stroke with LVO.