Timmonsacosta7901

COVID-19 pandemic caused by SARS-CoV-2 infection severely threatens global health and economic development. No effective antiviral drug is currently available to treat COVID-19 and any other human coronavirus infections. We report herein that a CFDA-approved macrolide antibiotic, carrimycin, potently inhibited the cytopathic effects (CPE) and reduced the levels of viral protein and RNA in multiple cell types infected by human coronavirus 229E, OC43, and SARS-CoV-2. Time-of-addition and pseudotype virus infection studies indicated that carrimycin inhibited one or multiple post-entry replication events of human coronavirus infection. Ipatasertib datasheet In support of this notion, metabolic labelling studies showed that carrimycin significantly inhibited the synthesis of viral RNA. Our studies thus strongly suggest that carrimycin is an antiviral agent against a broad-spectrum of human coronaviruses and its therapeutic efficacy to COVID-19 is currently under clinical investigation.

Transcranial magnetic stimulation (TMS) is an evidence-based treatment for pharmacoresistant major depressive disorder (MDD). In the last decade, the field has seen significant advances in the understanding and use of this new technology. This review aims to describe the large, randomized controlled studies leading to the modern use of rTMS for MDD. It also includes a special section briefly discussing the use of these technologies during the COVID-19 pandemic.

Several new approaches and technologies are emerging in this field, including novel approaches to reduce treatment time and potentially yield new approaches to optimize and maximize clinical outcomes. Of these, theta burst TMS now has evidence indicating it is non-inferior to standard TMS and provides significant advantages in administration. Recent studies also indicate that neuroimaging and related approaches may be able to improve TMS targeting methods and potentially identify those patients most likely to respond to stimulation.

While new data is promising, significant research remains to be done to individualize and optimize TMS procedures. Emerging new approaches, such as accelerated TMS and advanced targeting methods, require additional replication and demonstration of real-world clinical utility. Cautious administration of TMS during the pandemic is possible with careful attention to safety procedures.

While new data is promising, significant research remains to be done to individualize and optimize TMS procedures. Emerging new approaches, such as accelerated TMS and advanced targeting methods, require additional replication and demonstration of real-world clinical utility. Cautious administration of TMS during the pandemic is possible with careful attention to safety procedures.

Management of anaphylaxis during the SARS-CoV-2 pandemic should consider local infection rates so as to not burden local ED at times of pandemic, while also protecting patients from infection risks and progression of anaphylaxis. In this review, we identify a treatment strategy for anaphylaxis that balances the risks versus benefits of ED versus home management in this unprecedented time.

Physicians and patients have had to adapt new approaches to medical care during the SARS-CoV-2 pandemic due to restricted access to health care facilities. Telemedicine has substituted in-person visits, and such a drastic change in the patient care paradigm presents a need to revise the acute management of anaphylaxis.

Physicians should utilize telemedicine during this time to engage in shared decision-making with patients and their families to devise an anaphylaxis plan of management that emphasizes home care when symptoms are mild with an exception for ED care if a patient has had severe, near-fatal anaphylaxis episodes in the past. Previous anaphylaxis recommendations should remain in place despite the pandemic, including prompt use of epinephrine when needed, avoidance of known allergens, training of patients and their caregivers, and carrying of epinephrine autoinjector devices at all times to remain prepared in the event of an anaphylaxis episode.

The online version contains supplementary material available at 10.1007/s40521-021-00284-0.

The online version contains supplementary material available at 10.1007/s40521-021-00284-0.

Stock market process is full of uncertainty; hence stock prices forecasting very important in finance and business. For stockbrokers, understanding trends and supported by prediction software for forecasting is very important for decision making. This paper proposes a data science model for stock prices forecasting in Indonesian exchange based on the statistical computing based on R language and Long Short-Term Memory (LSTM).

The first Covid-19 (Coronavirus disease-19) confirmed case in Indonesia is on 2 March 2020. After that, the composite stock price index has plunged 28% since the start of the year and the share prices of cigarette producers and banks in the midst of the corona pandemic reached their lowest value on March 24, 2020. We use the big data from Bank of Central Asia (BCA) and Bank of Mandiri from Indonesia obtained from Yahoo finance. In our experiments, we visualize the data using data science and predict and simulate the important prices called Open, High, Low and Closing (OHLC) with various parameters.

Based on the experiment, data science is very useful for visualization data and our proposed method using Long Short-Term Memory (LSTM) can be used as predictor in short term data with accuracy 94.57% comes from the short term (1year) with high epoch in training phase rather than using 3years training data.

Based on the experiment, data science is very useful for visualization data and our proposed method using Long Short-Term Memory (LSTM) can be used as predictor in short term data with accuracy 94.57% comes from the short term (1 year) with high epoch in training phase rather than using 3 years training data.Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT's database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform.As blockchain technology booms, modern electronic voting system leverages blockchain as underlying storage model to make the voting process more transparent, and guarantee immutability of data. However, the transparent characteristic may disclose sensitive information of candidate for all system users have the same right to their information. Besides that, the pseudo-anonymity of blockchain will lead to the disclosure of voters' privacy and the third-parties such as registration institutions involved in voting process also have possibility of tampering data. To overcome these difficulties, we apply authority management mechanism into blockchain-based voting systems. In this paper, we put forward AMVchain, a fully decentralized and efficient blockchain-based voting system. AMVchain has a three-layer access control architecture, and on each layer, smart contracts are responsible for validation and granting permissions. Linkable ring signature is adopted in the process of voting to protect ballot-privacy. AMVchain also makes a tradeoff between efficiency and concurrency by introducing proxy nodes. The experiments results show that our system meets the basic requirements under the high concurrent users circumstance.

Early in the epidemic of coronavirus disease 2019, the Chinese government recruited a proportion of healthcare workers to support the designated hospital (Huoshenshan Hospital) in Wuhan, China. The majority of front-line medical staff suffered from adverse effects, but their real health status during COVID-19 epidemic was still unknown. The aim of the study was to explore the latent relationship of the physical and mental health of front-line medical staff during this special period.

A total of 115 military medical staff were recruited between February 17th and February 29th, 2020 and asked to complete questionnaires assessing socio-demographic and clinical characteristics, self-reported sleep status, fatigue, resilience and anxiety.

55 medical staff worked within Intensive Care and 60 worked in Non-intensive Care, the two groups were significantly different in reported general fatigue, physical fatigue and tenacity (

). Gender, duration working in Wuhan, current perceived stress level and health statu.Domestic violence, a prevalent problem in India, saw an increase during the lockdown imposed to contain the spread of COVID-19. This article explores the factors associated with an increase in domestic violence incidents during COVID-19 by applying routine activity theory (RAT) framework. Data were drawn from the incidents of domestic violence reported in newspapers. Data was analyzed using content analysis and three major themes, i.e., three principle components of RAT-motivated offender, suitable target, and absence of capable guardian-were drawn. Findings reveal that sources of motivation in domestic violence perpetrators during the lockdown were alcohol and unemployment. The symbolic value that perpetrators associated with women, lower inertia, visibility, and accessibility to the perpetrators made women suitable targets of domestic violence. Lastly, shortage of police force and travel restrictions on formal and informal sources resulted in the absence of capable guardians. We conclude that changes in the routine activities of people during the COVID-19 lockdown provided more opportunities to the perpetrators of domestic violence.

Traditional mindfulness-based interventions have been shown to reduce depression symptoms in pregnant women, although in-person classes may pose significant accessibility barriers, particularly during the COVID-19 pandemic. Mobile technology offers greater convenience, but little is known regarding the efficacy of self-paced, mobile-delivered (mHealth) mindfulness interventions in this population. This study tested the feasibility and acceptability of offering such an intervention for pregnant women with moderate-to-moderately-severe depression symptoms.

We conducted a single-arm trial within Kaiser Permanente Northern California (KPNC). Participants were identified through KPNC's universal perinatal depression screening program. Eligible participants included English-speaking pregnant women (<28 weeks of gestation) with moderate-to-moderately-severe depressive symptoms without a regular (<3 times/week) mindfulness/meditation practice. Participants were asked to follow a self-paced, 6-week mindfulness meditation program using a mobile app, Headspace™, 10-20 min/day.