In a report released yesterday, Tyler Van Buren from TD Cowen maintained a Buy rating on Gilead Sciences (GILD – Research Report), with a ...

Gilead Sciences: Strong Financial Performance and Promising Outlook Underpin Buy Rating | Markets Insider

The Speculation Around an AstraZeneca-Gilead Merger AstraZeneca, the UK’s largest drugmaker valued at $140bn, has reportedly contacted Gilead Sciences, an American biopharmaceutical company with a market value of approximately $96bn, regarding a possible merger deal.

AstraZeneca and Gilead Sciences merger would be the biggest in the pharma history

Companies with a long history of paying dividends and consistently hiking them remain appealing to income-focused investors. Gilead Sciences, Verizon Communications and ManpowerGroup have rewarded their shareholders for several decades and recently ...

Gilead Sciences, Verizon Communications And ManpowerGroup Are Winners For Passive Income

The latest trading day saw Gilead Sciences (GILD) settling at $83.08, representing a -0.1% change from its previous close.

Gilead Sciences (GILD) Stock Sinks As Market Gains: Here's Why

NORTHAMPTON, MA / ACCESSWIRE / September 12, 2024 / Each scientific discovery gets us one step closer to our shared goal of ending the HIV epidemic. As a leader in HIV treatment and prevention, we'll continue to build on our legacy and create innovative ...

Helping End HIV - Gilead Sciences

(Reuters) -Gilead Sciences&#x27; Trodelvy failed to improve survival for patients with advanced bladder cancer and only modestly extended the lives of previously treated patients with late-stage lung cancer in a pair of clinical trials, raising questions about growth prospects for the medicine.

Gilead's Trodelvy fails bladder cancer trial, modestly extends lung cancer survival

Gilead Sciences, Inc.&#x27;s (GILD) confirmatory TROPiCS-04 study on Trodelvy in locally advanced or metastatic urothelial cancer fails to achieve its primary endpoint.

Gilead's (GILD) Urothelial Cancer Study Did Not Meet Its Primary Goal

In addition to missing the mark in overall survival, Gilead reported Thursday a higher number of deaths in the Trodelvy arm of the confirmatory metastatic urothelial cancer study.

Gilead’s Trodelvy Fails to Reach Primary Endpoint in Confirmatory Trial | BioSpace

The FDA paused enrollment in the only remaining tests of the drug, as Gilead reviews the program it acquired through a buyout of biotech Forty Seven.

Gilead reviewing drug acquired in $5B buyout after latest setback

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As the world becomes increasingly digital, nearly every company is looking for to code—no matter the industry. The phenomenon has been great news for software engineers and programmers who can now command high salaries for their hot, in-demand talent. software engineers Perhaps it is no surprise that software engineers in the San Francisco Bay Area, home to Silicon Valley and its many tech giants, can command an even higher premium with their pay packages. According to the more than 5 million verified professionals on the professional social network Blind, these are the top 17 best-paying cities for software engineers in the San Francisco Bay Area. Blind considered the annual base pay and the total compensation package, including the annual salary and any stock-based compensation or bonuses a company might offer. 1. Los Gatos, California n: $500,449 Average Total Compensatio : $475,000 Median Total Compensation : $470,015 Average Salary : $451,000 Median Salary Los Gatos is the best-paying city for software engineers in the U.S., according to Blind. The small town is home to Netflix, which prefers to hire more senior or experienced professionals and pays accordingly. The video-streaming giant typically pays large salaries and gives few employees stock-based compensation. 2. Menlo Park, California : $328,423 Average Total Compensation : $297,000 Median Total Compensation : $167,004 Average Salary : $165,000 Median Salary Menlo Park might be home to only 34,000 people, but its population can swell in size with tech workers during the workweek. Menlo Park is home to the headquarters of Meta, the owner of Facebook and one of the highest-paying technology companies. Google and Roblox were also founded in the city. 3. Cupertino, California n: $279,391 Average Total Compensatio : $255,850 Median Total Compensation : $165,753 Average Salary : $165,000 Median Salary Cupertino is home to Apple and its futuristic Apple Park corporate campus. Software engineers at Apple have enjoyed large paychecks recently, as the iPhone maker briefly topped a jaw-dropping $3 trillion market cap early in 2022. 4. San Bruno, California : $278,368 Average Total Compensation : $225,900 Median Total Compensation : $161,269 Average Salary : $149,500 Median Salary YouTube moved its headquarters to San Bruno in 2007 and has become the city’s largest employer. The Google-owned video streaming service employs more than 2,000 professionals. 5. Mountain View, California : $275,181 Average Total Compensation : $242,000 Median Total Compensation : $160,790 Average Salary : $156,000 Median Salary Mountain View is synonymous with Silicon Valley. Long before startups were birthed in San Francisco Bay Area garages, the city was home to silicon semiconductor producers, which gave the region its name. Of course, the city is now well-known as the headquarters of Google, but other tech companies with prominent presences include 23andMe, Intuit, Mozilla and Synopsys. 6. Oakland, California : $266,869 Average Total Compensation : $197,750 Median Total Compensation : $192,240 Average Salary : $168,750 Median Salary Oakland has become a hub for many financial-technology companies. Credit Karma moved its headquarters from San Francisco to downtown Oakland in 2020. Block and Marqeta also have major offices in “The Town.” 7. San Francisco, California : $265,050 Average Total Compensation : $225,000 Median Total Compensation : $165,042 Average Salary : $160,200 Median Salary San Francisco might be the crown jewel of the technology industry, as the home of too many tech startups and companies to count. The city by the bay is home to Airbnb, DoorDash, Lyft, Twitter, Uber and more, but its top employer is . The cloud giant employs more than 9,000 employees, and its headquarters has made a permanent contribution to the downtown skyline. Salesforce 8. Sunnyvale, California : $260,048 Average Total Compensation : $238,000 Median Total Compensation : $160,499 Average Salary : $160,000 Median Salary Sunnyvale maintains its Silicon Valley roots as home to the chip-maker AMD and advanced technology and networking companies, including Juniper Networks, Microsoft-owned LinkedIn and NetApp. 9. Palo Alto, California : $234,739 Average Total Compensation : $200,000 Median Total Compensation : $157,488 Average Salary : $154,000 Median Salary The tony city of Palo Alto is one of the major cities of Silicon Valley. Cloudera and VMware are some of the largest technology companies based in the city. 10. San Mateo, California : $234,066 Average Total Compensation : $187,875 Median Total Compensation : $158,629 Average Salary : $155,500 Median Salary Companies that call San Mateo home include Coupa, Roblox and Snowflake. Before being acquired by Zendesk, Momentive, which was founded as SurveyMonkey, was headquartered in the city. 11. Pleasanton, California : $225,058 Average Total Compensation : $202,000 Median Total Compensation : $150,887 Average Salary : $150,000 Median Salary Pleasanton is home to many business parks, but the suburb’s top employer is the HR-technology giant Workday. 12. Foster City, California : $220,293 Average Total Compensation : $190,000 Median Total Compensation : $158,319 Average Salary : $148,500 Median Salary Foster City is the headquarters site of the biopharmaceutical giant Gilead Sciences and the enterprise software company Zuora, among other companies. 13. Fremont, California : $218,913 Average Total Compensation : $180,000 Median Total Compensation : $144,638 Average Salary : $142,500 Median Salary Notable companies with headquarters in Fremont include Corsair, Synnex and more, although the city’s No. 1 employer is Tesla. 14. Santa Clara, California : $214,168 Average Total Compensation : $197,000 Median Total Compensation : $153,885 Average Salary : $150,000 Median Salary AMD, Intel, NVIDIA and Palo Alto Networks are some of the most well-known tech companies headquartered in Santa Clara. 15. San Jose, California n: $210,801 Average Total Compensatio : $183,000 Median Total Compensation : $153,390 Average Salary : $145,000 Median Salary The largest city by population in the San Francisco Bay Area, San Jose is home to Adobe, Cisco, eBay, PayPal, Zoom and more. 16. Redwood City, California : $207,955 Average Total Compensation : $190,000 Median Total Compensation : $156,248 Average Salary : $151,000 Median Salary Redwood City is the headquarters location for several global technology companies, such as Box, Carbon, Electronic Arts and Informatica. 17. Milpitas, California : $188,146 Average Total Compensation : $165,750 Median Total Compensation : $154,073 Average Salary : $138,000 Median Salary Some of the top employers in Milpitas are tech employers, including Cisco, FireEye, SanDisk and more. Methodology Blind analyzed the base salaries and total compensation of software engineers in the San Francisco Bay Area on its platform. Cities were ranked by the average total compensation, including annual base salary and, if applicable, stock-based compensation and cash bonuses. All data is self-reported by verified professionals on the professional social network Blind. Also published on Teamblind's blog

17 Highest-Paying Cities in the San Francisco Bay Area for Software Engineers

Las 17 ciudades que mejor pagan en el área de la bahía de San Francisco para ingenieros de software

17 villes les mieux rémunérées de la région de la baie de San Francisco pour les ingénieurs logiciels

सॉफ्टवेयर इंजीनियरों के लिए सैन फ्रांसिस्को खाड़ी क्षेत्र में 17 उच्चतम-भुगतान वाले शहर

ソフトウェア エンジニアにとって、サンフランシスコ ベイエリアで最も給料の高い 17 の都市

17 cidades mais bem pagas na área da baía de São Francisco para engenheiros de software

17 thành phố trả lương cao nhất trong khu vực vịnh San Francisco cho các kỹ sư phần mềm

旧金山湾区软件工程师薪酬最高的 17 个城市

On the road to better healthcare So, how do I see the future of healthcare using AI? Well, let’s just face it. AI is heading to transform medicine and somewhere even replace real-medicine workers. Every year we observe the appearance of new and more advanced solutions. This, by the way, provides a whole slew of advantages, one of the most important of them is reducing the time needed to reach a diagnosis that allows medical workers to better prioritize patient case. Right now there is already a sufficient number of successful developments. AI and deep learning can analyze a lot more factors and cases than live-health workers do. To be more precise, we are able to use AI for genome research, drug development, medical imaging. AI-based devices can learn, analyze large amounts of information, and make decisions much faster than humans. Wow.. isn’t it just amazing? But what does it mean and is it really so? I suggest reviewing some of the recent advancements and startups to figure it out! and also for a healthy life check out these 12 amazing Healthy lifestyle facts . AI & Deep Learning: Current Status and Perspectives in Medicine In medical practice, there is such an important thing as medical intuition. When a patient visits a doctor, the doctor just looks and literally asks a couple of questions and he already knows what is going on and what disease a patient may have. Let’s look at this from a data analysis perspective. So, what is medical intuition? It is like a doctor using his built-in neural network. Having analyzed a sufficient number of cases, a doctor is able, consciously or not, to identify some additional factors that help him narrow down the solution space. And further, he comes to hypotheses, which he considers a possible diagnosis. Is AI better than medical intuition? Whether we like it or not, medical intuition is an automated thing that can be performed not less well by a machine. What is more, some diagnoses can be formulated better by machines. Let’s take a look at the most prominent cases. Top Recent AI Startups in The Medical Field This is a service that allows you to directly communicate with a person and give him your recommendations. The medical application communicates with the patient, asks about symptoms and complaints, and in response gives recommendations, including which doctor to visit, offers to contact a specialist for a remote consultation. Ada. About 20% of lung cancer and breast cancer go unnoticed in the diagnosis. Lunit is developing deep learning and 3D visualization software to help detect difficult-to-diagnose diseases such as airway cancer. Its use increases the probability of detecting cancer to 83–86%. Over time, the technology will improve and accuracy will become even higher. Lunit. This program monitors the condition of people who have recently undergone long-term treatment or suffer from chronic diseases. It was developed by a startup from San Francisco and raised 8 million venture capital investments initially. The application was created in order to structure data on the state of a person, it sends them to a specialist and gives recommendations. Also, the system is able to remind about the time of taking medications and when there is a need to visit a doctor. Sense.ly. The creation of new drugs is one of the most important tasks of modern medicine. The development of a single medicine in a research laboratory costs $ 2.6 billion, in total about $ 150 billion is spent on this process. Therefore, in 2014, only 46 new drugs appeared. Insilico Medicine creates artificial intelligence that will help in the development of drugs, biomarkers and the study of aging mechanisms. The company wants to improve the quality of life of every person. Insilico Medicine. (Boston) was founded by Andy Back, a graduate of Harvard Medical School. Back spent more than five years studying at Harvard and decided to help other doctors with the timely diagnosis of complex diseases, such as cancer, using machine learning to quickly and accurately analyze cell images. PathAI Startup opportunities are used not so much by doctors as by researchers at pharmaceutical companies. Among the startup client’s are such giants of the pharmaceutical industry like Novartis, Gilead Sciences and Bristol-Myers Squibb. (San Diego) helps blind and visually impaired people “see” the world by combining the capabilities of the human body and the capabilities of artificial intelligence in an application or smart glasses. The company claims that their AI development, Chloe, is still at the initial level of its capabilities: it can perform simple tasks like reading instructions on a medicine package, but the developers have big ambitions for its further development. Aira One of the company’s goals is to make navigation possible with computer vision. The product can be used free of charge for sessions shorter than five minutes and for all sessions in more than 25 thousand places where Aira has partnership agreements. For example, JFK Airport or the Wegmans chain of stores. Diagnostics Systems Using Julia Language Such programming languages like Python and R are popular for applications of machine learning. But in recent years, Julia is acquiring its place and has become the new de-facto for machine learning. Julia offers best-in-class support for modern ML-frameworks like TensorFlow and MXNet, making it easy to adapt to existing workflows. Here are some of the powerful projects built on this language. IBM and Julia Computing for Diagnosing Diabetic Retinopathy Diabetic retinopathy is a complication of diabetes, caused by high blood sugar levels damaging the back of the eye (retina). This disease affects more than 126 million diabetics. Timely screening and diagnosis can help prevent vision loss for millions of diabetics worldwide, but many of them lack access to health care. There are over 126.2 million people in the world (as of 2010) with diabetic retinopathy, and this is expected to rise to over 191.2 million by 2030. According to the WHO in 2006, it accounted for 5% of world blindness. Deep Learning in Julia comes to the rescue and helps to significantly reduce this number. By combining Julia’s superior speed and performance with IBM’s Power8 server and NVIDIA Tesla K80 GPU accelerators, researchers increased image processing speed 57x — a dramatic improvement. They used MXNet.jl, a Julia package for deep learning. As a first step, they load a pre-trained model — ImageNet model called Inception with weights in their 39th epoch. On top of that, they specify a simple classifier. contextflow and Julia Help Radiologists Diagnose Difficult Cases Faster The first and foremost problem with the current state of radiologists’ working process is increasing workloads and lack of time. For diagnosing and treating disease, radiologists need to spend more time overall searching for information and a smaller proportion of their time actually interpreting examinations. contextflow’s image search engine in collaboration with Julia made efforts to resolve this dilemma. contextflow is a 3D image-based search engine that uses deep learning to put the knowledge encoded in millions of medical images and reports at fingertips. Simply mark a region of interest in an image, contextflow returns reference cases, and associated knowledge. While radiologists spend up to 20 minutes searching for information, contextflow using Julia promises to cut down search time to ~2 seconds. Taking into account a global shortage of radiologists, this could be a real solution. For doing so, contextflow uses Knet.jl — a great low-level Julia deep learning package, to build the models to identify reference cases for physicians. More Ways AI Is Making an Impact on Medical Care Google Deepmind Predicts an Exacerbation of Kidney Disease Two Days Before It Occurs This deep learning method can continuously predict the risk of future patient deterioration, drawing on recent work that models adverse events based on electronic medical records and using acute kidney damage — a common and potentially life-threatening condition — as an example. The model was developed on a large data set of electronic medical records covering various clinical environments, which included 703,782 adult patients in 172 inpatients and 1,062 outpatient settings. The model predicts 55.8% of all stationary episodes of acute kidney damage and 90.2% of all acute kidney damage that required subsequent dialysis, with a lead time of up to 48 hours and a ratio of 2 false alerts for each true alert. In addition to predicting future acute kidney damage, the model provides confidence estimates and a list of clinical features that are most common for each prognosis, as well as predicted future trajectories for clinically relevant blood tests. Despite the fact that recognition and quick treatment of acute kidney damage is known to be difficult, the developed approach may offer opportunities for identifying patients at risk within a time interval that allows for early treatment. Augmented Reality Microscope with Real-Time Integration of Artificial Intelligence for Cancer Diagnosis Microscopic evaluation of tissue samples plays an important role in the diagnosis and formulation of cancer and, therefore, directs therapy. However, these estimates demonstrate significant variability, and many regions of the world do not have access to trained pathologists. Although artificial intelligence promises to improve access and quality of care, the cost of digitizing images for pathology and the difficulty in deploying AI solutions remain barriers to real use. Augmented Reality Microscope (ARM) superimposes AI-based information on the current sample presentation in real-time, providing seamless AI integration into routine workflows. Researchers demonstrate the usefulness of ARM for detecting metastatic breast cancer and identifying prostate cancer, with latency compatible with real-time use. It is expected that ARM will be able to remove barriers to the use of AI designed to improve the accuracy and efficiency of a cancer diagnosis. Neural Network to Instantly Predict the Tertiary Structure of the Protein Another powerful breakthrough in healthcare made by AI. ProteinNet neural network is able to predict the structure of a protein in milliseconds. Well, predicting the structure of a protein from its sequence is a central biochemistry problem. Convolutional neural networks show promising results. Advances in deep learning that replace complex, human-developed conveyors with differentiable models optimized from start to finish suggest potential benefits. In their study, scientists presented an end-to-end differentiable model to study protein structure. The model links the local and global structure of the protein through geometric units that optimize global geometry without breaking local covalent chemistry. The tested model uses two complex tasks: forecasting new folds without co-evolutionary data and forecasting known folds without structural patterns. In the first problem, the model achieves modern accuracy, and in the second, it is within 1–2 Å; competing methods that use co-evolution and experimental patterns have been refined over the years, and it is likely that the differentiated approach has significant potential for further improvement, ranging from drug discovery to protein construction. Certainly, there are lots of other AI applications in medical care. This means only one thing for me, we have great potential for further development. Final word: What if AI do a mistake? For me now everything looks fine with AI plus medicine except for one thing. While cutting-edge technologies save time, money and serve patients more efficiently, the majority are curious — is it safe enough? What if AI software will give the wrong diagnosis and so on. Who will be responsible for the false results? It’s hard to answer. From the legal point of view, we do not understand who to shift the responsibility to. What’s the solution to this? Well, I think, medical workers should supervise the work of AI. In my opinion, it is highly important for the medicine to change not towards the complete replacement of doctors, but towards symbiosis and joint collaboration with AI. All existing AI-powered systems should act like supporting systems for making medical decisions. Also read - The Healthy Humans blogs Thanks for reading!

Artificial Intelligence & Deep Learning for Medical Diagnosis

Last December, stories started surfacing of a new virus affecting the Chinese city of Wuhan. At the epicenter of the outbreak, Chinese officials started building new hospitals to aid the people in need. What started out as something that looked like a flu epidemic, soon escalated into a full blown pandemic, called the novel coronavirus (SARS-CoV-2). As numbers of infected people are on the rise, the impact on people’s lives continues to grow, beyond anything most of us have ever witnessed in our lives. Social distancing and thorough hand washing seem to be the primary action humans need to abide by in order to slow down the spreading of the virus. On the 19th of March, US President Trump announced three promising drug treatments to battle the coronavirus. Two of them, chloroquine and hydroxychloroquine have been approved by the FDA to start clinical trials on treating the coronavirus. The other is Regeneron, a drug that has previously been used to successfully treat people infected with the Ebola virus. World Health Organization (WHO) Senior Advisor, Bruce Aylward, stated, ‘There’s only one drug right now that we think will have real efficacy, and that’s remdesivir’. Two US companies, and (starting the 23rd of March) have started the process of rolling out home COVID-19 tests. The Nurx one is projected to cost around $181 while Everlywell sells them for $135. The initial supply is low, but they project to be able to scale up manufacturing soon. Nurx will be selling their supply each morning, Monday through Saturday, at 7 AM PST, until stocks run out. Nurx Everlywell I would like to emphasize that I am not an expert in this field, all the information stated in this article is based on reliable sources (official government or national health authorities), field experts like virologists and research papers on the topic. At least until March 20th, there was not yet any proven antidote against the coronavirus. Chloroquine As a medicine used to treat malaria, chloroquine, has been discovered in 1934, , but it had been abandoned for decades as it was too toxic to be prescribed to humans. by Hans Andersag Back in 2004, when the SARS epidemic arose, lab-tested Chloroquine as an anti-virus and it worked. When the time came to test real-life patients the virus had stopped spreading, so there were no people left for clinical trial. Chinese researchers notices quicker recoveries and milder symptoms in COVID19 patients that were given chloroquine. researchers from KU Leuven Researchers of the Rijksuniversiteit Groningen in The Netherlands, are as a means to battle the coronavirus. Paul Hagedoorn and Erik Frijlink from the Rijksuniversiteit are working on cultivating the medicine so it can be used with an inhaler. This way, the medicine is directed straight into the place where it needs to be, the lungs. testing chloroquine and hydroxychloroquine The only manufacturer of chloroquine located in the US (New Jersey), Rising Pharmaceuticals, amidst the increasing amount of people that got infected with the coronavirus. raised the price of chloroquine in January by 100%, Among others, chloroquine producers are located in: , , and . India, China The Netherlands The United States Hydroxychloroquine Hydroxychloroquine, or better known as Plaquenil (brandname), is a working substance used to prevent and treat malaria. It is also widely available to treat auto-immune diseases and rheumatic arthritis. While chloroquine can be highly toxic, an overdose can result in poisoning or death, hydroxychloroquine is about 40% less toxic. A recent , show positive results with patients that were administered hydroxychloroquine as a treatment to COVID-19. The preliminary results look promising, although the trial group was very small (20 patients in the treatment group, 16 in control group). In the treatment group, 6 patients were administered hydroxychloroquine in combination with azithromycin. Within 5 days all 6 patients tested negative on the coronavirus. The treatment group patients that solely received hydroxychloroquine had a 50% decrease in positive testing for COVID-19 after 6 days. Almost 90% of patients in the control group still tested positive for COVID-19 after 6 days. In Tweet, by @AppleHelix you can see the difference between groups very clear. clinical trial, in France this Regeneron and Sanofi Regeneron Pharmaceuticals and Sanofi are working on getting Kevzara approved to be a treatment option for the novel coronavirus. Kevzara is officially a drug that is being used to treat rheumatic arthritis. The clinical trial will research the effect kevzara has on reducing inflammation in the lungs. This isn’t the only COVID-19 related trial they have planned. Scientists at Regeneron are testing on mice that have been genetically modified to have a working human immune system. The company aims to start clinical trials by early summer. The trial consists of a multi-antibody cocktail to be administered to people to treat the coronavirus either before or after infection. Remdesivir In the state of Nebraska, a has started on the 25th of February, using remdesivir as a drug to fight tcoronavirus. Remdesivir, manufactured by , is a broad-spectrum antiviral treatment. Initially they wanted to get the drug approved as a cure to Ebola. clinical trial Gilead Sciences Inc Remdesivir has not been officially approved by any national health organization yet, but the manufacturer is working closely with various organizations to start clinical testing on COVID-19 patients. In the past remdesivir has been successfully tested on rats, as a working antivirus against SARS and MERS infections. According to their website, Gilead Sciences has eight locations in the US. On March 17th remdesivir has been as a drug to treat COVID-19 patients with severe symptoms in the Czech Republic. officially approved Concluding remarks After doing this research, I must say I am surprised by the overall abilities to use existing medicine to treat COVID-19. The research that has been previously done on anti-virus drugs to treat SARS and MERS, can be further built upon with coronavirus patients after approval from the proper authorities. However, most treatments are for faster recovery and/or reducing the symptoms. Besides the four drugs discussed in this articles, there are also other promising anti-virus medicine that are being tested at the moment. In China, COVID-19 patients show positive results after using favipiravir, in a clinical trial. (developed by a subsidiary of Fujifilm) is used in Japan as a treatment against the flu. Patients that took part in the trial recovered sooner from the Coronavirus (after 4 days median, in oppose to 11 days, without favipiravir). Their X-rays also showed improvement in lung conditions with over 90% of clinical trial participants. starts a clinical trial, on a new Coronavirus vaccine, at the end of April. Favipiravir Innovio Pharmaceuticals When I check out the in my home country on how to treat COVID-19, I noticed that patients can request to be treated with Chloroquine, Remdesivir or Hydroxychloroquine, but only if they sign a waiver stating the care givers are not at fault when the patients’ situation decreases. They further state that the drugs have not been tested properly on people with the coronavirus, so it is no official treatment drug yet. country-wide guidelines I hope clinical testing with the previously mentioned drugs will have a positive effect on COVID-19 patients. I applaud national health organizations for stepping up to the plate and working around the clock to get clinical trials approved as soon as possible. Hopefully a drug treatment will be approved and the medicine will be available at a reasonable cost for everyone. Some interesting reads about clinical trail related to treating COVID-19 patients: https://blogs.sciencemag.org/pipeline/archives/2020/03/06/covid-19-small-molecule-therapies-reviewed http://www.chinadaily.com.cn/a/202003/17/WS5e708666a31012821727fcbd.html https://www.clinicaltrialsarena.com/analysis/coronavirus-mers-cov-drugs/ Subscribe to my channels , , and if you like my articles and would like to be informed about Blockchain, cryptocurrency projects and news. You can also read my articles on . Steemit Medium Twitter LinkedIn If you have any questions about this article, please comment in the comment section below. Thank you! You can contact me on Telegram @LindaCrypto. LindaCrypto

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