Check out this mnemonic if you have difficulty memorizing it :)
Thursday, April 30, 2020
Metronidazole : spectrum of organisms covered mnemonic
Hello
Metronidazole has bactericidal activity against
Protozoa:
- Giardia
- Entamoeba
- Trichomonas
Bacteria: (anaerobic gram-negatives)
- Bacteroides
- Clostridium sp (doc in pseudomembranous colitis)
- GARDNerella
- H. pylori
mnemonic: GET BaC in the GARDEN, Hippo
- Jaskunwar Singh
Metronidazole has bactericidal activity against
Protozoa:
- Giardia
- Entamoeba
- Trichomonas
Bacteria: (anaerobic gram-negatives)
- Bacteroides
- Clostridium sp (doc in pseudomembranous colitis)
- GARDNerella
- H. pylori
mnemonic: GET BaC in the GARDEN, Hippo
- Jaskunwar Singh
Wednesday, April 29, 2020
Red man syndrome - ADR of vancomycin
Hello
Red man syndrome, RMS is an adverse reaction to rapid infusion of vancomycin that leads to flushing/redness of face, neck and upper torso. The mechanism of this reaction is histamine release (anaphylactoid reaction).
Prevention - slow IV infusion of vancomycin over 1 hr
PS - I remember this as lots of wine causes flushing, so does v(w)ancomycin ;D
That's all
- Jaskunwar Singh
Red man syndrome, RMS is an adverse reaction to rapid infusion of vancomycin that leads to flushing/redness of face, neck and upper torso. The mechanism of this reaction is histamine release (anaphylactoid reaction).
Prevention - slow IV infusion of vancomycin over 1 hr
PS - I remember this as lots of wine causes flushing, so does v(w)ancomycin ;D
That's all
- Jaskunwar Singh
Clinical vignette: Meningitis due to Listeria monocytogenes
Hello
Listeria monocytogenes is the 3rd most common organism that causes bacterial meningitis.
Cephalosporins do not cover this gram - positive bacteria under its spectrum. More aptly saying, the cephs do not kill this bacteria. So, especially in high-risk patients such as neonates, elderly, and the immunocompromised, cephalosporins are given in combination with ampicillin, and never alone.
Ceftriaxone is avoided for use in neonates due to its decreased biliary metabolism and sludging.
The choice of ceph in neonates and other high-risk groups in the case of meningitis is cefotaxime.
That's all
- Jaskunwar Singh
Listeria monocytogenes is the 3rd most common organism that causes bacterial meningitis.
Cephalosporins do not cover this gram - positive bacteria under its spectrum. More aptly saying, the cephs do not kill this bacteria. So, especially in high-risk patients such as neonates, elderly, and the immunocompromised, cephalosporins are given in combination with ampicillin, and never alone.
Ceftriaxone is avoided for use in neonates due to its decreased biliary metabolism and sludging.
The choice of ceph in neonates and other high-risk groups in the case of meningitis is cefotaxime.
That's all
- Jaskunwar Singh
Tuesday, April 28, 2020
Clinical correlate: Sildenafil contraindicated for pilots
Hello
PDE-6 is present in the eyes.
What is AI and Why You Should Be Excited About It
In 1950, Alan Turing asked, “Can machines think?”. Fast-forward to 2010 and artificial intelligence can diagnose diseases, fly drones, translate between languages, recognise emotions, trade stocks and even beat humans at Chess and Go.
Artificial Intelligence (AI), in essence, is machines mimicking human intelligence. Now, that can be of two types:
1. One that's already here, narrow AI, where a computer performs some very specific task. Take for example, Apple's Siri or Netflix's recommender system.
2. The other, general AI, that remains science fiction for now. If you're thinking of Jarvis in “Iron Man" or R2-D2 in “Star Wars”, you're quite right.
An application of AI is Machine Learning, where the computer automatically improves at performing a task, with more experience. Deep Learning is a subset of machine learning that is more intensive; uses more data and more complex algorithms.
Now, as a community of medicos, why should we bother about tech at all? Well, the future of healthcare looks increasingly facilitated by technology. The aim is to shift from "treating illness" to "sustaining wellness"; to have have a more proactive, rather than a reactive, model of care delivery. AI will help redesign our services and better utilise our resources. The goal isn't to replace what humans do, but instead augment it.
Here are a few ways AI can potentially help medicine:
1. Image recognition and diagnostic radiography, eg: Qure.ai, and Stanford's CheXpert system
2. Preliminary diagnoses, eg: Babylon Health, and DeepMind's Streams application
3. Virtual nursing assistants, eg: Care Angel's virtual nurse assistant
4. Clinical trials participant identifier, eg: deep6.ai
5. Computer-assisted robotic surgery, eg: Heartlander miniature robot
6. 3D mapping and printing, eg: 3D printed heart stents
7. Administrative workflow assistance, eg: IBM Watson
8. Fraud detection and Cybersecurity, eg: H2O.ai
[The list is by no means exhaustive. I implore you to know more about the examples mentioned by simply copy-pasting them into Google search.]
To conclude, the future of healthcare looks exciting and will be far more collaborative than it is today, working in alliance with AI, data science, statistics, engineering, and genomics. The ultimate objective is always to improve quality of treatment and patient outcomes.
Author's note: If, as a med student or a doctor, you're interested in kickstarting your own career towards AI and healthcare, please let me know in the comments. I will appropriately refer you to the relevant resources. To give you a brief background, I’ve worked with data scientists on seven medicine related portfolio projects, utilising machine and deep learning algorithms. I worked as a clinician and a programmer (have professional working proficiency in Python). Here’s my top 3:
1. Breast Cancer Detection Using Python & Machine Learning, with a model accuracy of 95% using artificial neural networks and support vector machine, on Wisconsin diagnostic data set
2. Identifying Skin Lesions Using Python & Deep Learning, with a model accuracy of 79% using convolution neural networks, on Cornell HAMNIST-10000 data set
3. Determining the Efficacy of Corrective Spinal Surgery in Childhood Kyphosis Using Python & Machine Learning, with a model accuracy of 88% using decision trees and random forest classifier on a Kaggle datset
Thank you for reading.
- Ashish Singh
Artificial Intelligence (AI), in essence, is machines mimicking human intelligence. Now, that can be of two types:
1. One that's already here, narrow AI, where a computer performs some very specific task. Take for example, Apple's Siri or Netflix's recommender system.
2. The other, general AI, that remains science fiction for now. If you're thinking of Jarvis in “Iron Man" or R2-D2 in “Star Wars”, you're quite right.
An application of AI is Machine Learning, where the computer automatically improves at performing a task, with more experience. Deep Learning is a subset of machine learning that is more intensive; uses more data and more complex algorithms.
Now, as a community of medicos, why should we bother about tech at all? Well, the future of healthcare looks increasingly facilitated by technology. The aim is to shift from "treating illness" to "sustaining wellness"; to have have a more proactive, rather than a reactive, model of care delivery. AI will help redesign our services and better utilise our resources. The goal isn't to replace what humans do, but instead augment it.
Here are a few ways AI can potentially help medicine:
1. Image recognition and diagnostic radiography, eg: Qure.ai, and Stanford's CheXpert system
2. Preliminary diagnoses, eg: Babylon Health, and DeepMind's Streams application
3. Virtual nursing assistants, eg: Care Angel's virtual nurse assistant
4. Clinical trials participant identifier, eg: deep6.ai
5. Computer-assisted robotic surgery, eg: Heartlander miniature robot
6. 3D mapping and printing, eg: 3D printed heart stents
7. Administrative workflow assistance, eg: IBM Watson
8. Fraud detection and Cybersecurity, eg: H2O.ai
[The list is by no means exhaustive. I implore you to know more about the examples mentioned by simply copy-pasting them into Google search.]
To conclude, the future of healthcare looks exciting and will be far more collaborative than it is today, working in alliance with AI, data science, statistics, engineering, and genomics. The ultimate objective is always to improve quality of treatment and patient outcomes.
Author's note: If, as a med student or a doctor, you're interested in kickstarting your own career towards AI and healthcare, please let me know in the comments. I will appropriately refer you to the relevant resources. To give you a brief background, I’ve worked with data scientists on seven medicine related portfolio projects, utilising machine and deep learning algorithms. I worked as a clinician and a programmer (have professional working proficiency in Python). Here’s my top 3:
1. Breast Cancer Detection Using Python & Machine Learning, with a model accuracy of 95% using artificial neural networks and support vector machine, on Wisconsin diagnostic data set
2. Identifying Skin Lesions Using Python & Deep Learning, with a model accuracy of 79% using convolution neural networks, on Cornell HAMNIST-10000 data set
3. Determining the Efficacy of Corrective Spinal Surgery in Childhood Kyphosis Using Python & Machine Learning, with a model accuracy of 88% using decision trees and random forest classifier on a Kaggle datset
Thank you for reading.
- Ashish Singh
Saturday, April 25, 2020
COVID-19: Whose Virus Is It Anyway? Possible origins of SARS-CoV-2
It's only reasonable you may want to know about the origins of the COVID-19 pandemic. After all, our lives have been affected, one way or the other. But was it the bat? Was it the pangolin? Or was it a lab experiment gone wrong? Let's look at the two most definitive evidence we have at hand: virus genomics and structure.
Evidence #1
The receptor binding domain (RBD) in the spike protein is the most variable part of the coronavirus family genome. SARS-CoV-2 seems to have an RBD that binds with high affinity to ACE2 from humans, and other species with high receptor homology. This RBD has six key amino acid residues.
Evidence #2
The second notable feature of SARS-CoV-2 is a polybasic cleavage site at the junction of S1 and S2, the two subunits of the spike. This allows effective cleavage by furin and other proteases and has a role in determining viral infectivity and host range. Insertion of proline to this site and subsequent addition of O-linked glycans are unique to SARS-CoV-2.
Keeping these in mind, we have:
Theory #1
Natural selection in animal before zoonotic transfer
As many early cases of COVID-19 were linked to the Huanan market in Wuhan, it is possible that an animal source was present at this location.
Given the similarity of SARS-CoV-2 to bat SARS-CoV-like coronaviruses, it is likely that bats serve as reservoir hosts for its progenitor. This "bat virus" or more formally, RaTG13 is nearly 96% identical to SARS-CoV-2. Its spike diverges in the RBD, which suggests that it may not bind efficiently to human ACE2.
Malayan pangolins illegally imported into Guangdong province contain coronaviruses similar to SARS-CoV-2. Some "pangolin coronavirus" exhibit strong similarity to SARS-CoV-2 in the RBD, including all six key RBD residues. This clearly shows that the SARS-CoV-2 spike protein optimised for binding to human-like ACE2 is the result of natural selection.
Neither the bat nor the pangolin coronavirus, however, has polybasic cleavage sites. This means, no animal coronavirus has been identified that is sufficiently similar to be the direct progenitor of SARS-CoV-2. That said, the diversity of coronaviruses in bats and other species is massively undersampled. Mutations, insertions and deletions can occur near the S1–S2 junction of coronaviruses, which shows that the polybasic cleavage site can arise by a natural evolutionary process. This perfectly sets us up for our next theory.
Theory #2
Natural selection in human after zoonotic transfer
It is possible that a progenitor of SARS-CoV-2 jumped into humans to acquire the genomic features described above through adaptation, during undetected human-to-human transmission. Once acquired, these adaptations would enable the pandemic to take off.
All SARS-CoV-2 genomes sequenced so far have the genomic features described above and are thus derived from a common ancestor that had them too. The "pangolin coronavirus" has an RBD very similar to that of SARS-CoV-2, by the process of natural selection. From this, we can infer the same happened with the virus that jumped to humans. So we can say, with some degree of confidence, the insertion of polybasic cleavage site occured during human-to-human transmission.
From what we know the first case of COVID-19 has been traced back to November 2019. This presumes a period of unrecognised human-to-human transmission, between the initial zoonotic event and the acquisition of the polybasic cleavage site.
Theory #3
Lab experiment gone wrong
Basic research involving passage of bat SARS-CoV-like coronaviruses in cell culture and animal models has been ongoing for many years in biosafety level 2 laboratories across the world, and there are documented instances of laboratory escapes of SARS-CoV. In theory, it is possible that SARS-CoV-2 acquired RBD mutations during adaptation to passage in cell culture.
Having said that, the "pangolin coronavirus" with nearly identical RBDs, provides a much stronger explanation of how SARS-CoV-2 acquired these via recombination or mutation. The high-affinity binding of the SARS-CoV-2 spike protein to human ACE2 is most likely the result of natural selection on a human or human-like ACE2.
The acquisition of both the polybasic cleavage site and predicted O-linked glycans also argues against culture-based scenarios. New polygenic cleavage sites have only been observed after prolonged in-vivo passage whereas generating O-linked glycans likely involves an immune system.
Furthermore, if genetic manipulation had been performed, one of the several reverse-genetic systems available for coronaviruses would probably have been used. However, the genetic data irrefutably show that SARS-CoV-2 is not derived from any previously used virus backbone.
These are strong arguments that SARS-CoV-2 is not the product of purposeful manipulation.
Conclusion
Theory #2 seems most likely, given the information currently available, but more scientific data could swing the balance of evidence to favour one hypothesis over another. What's important is to further study the possible origins, not just for understanding the current zoonotic pandemic but also to prevent the potential future ones.
References
1. 'The proximal origin of SARS-CoV-2' by Andersen et al: www.nature.com/articles/s41591-020-0820-9
2. 'A pneumonia outbreak associated with a new coronavirus of probable bat origin' by Zhou et al: www.nature.com/articles/s41586-020-2012-7
3. 'A new coronavirus associated with human respiratory disease in China' by Wu et al: www.nature.com/articles/s41586-020-2008-3
- Ashish Singh
Evidence #1
The receptor binding domain (RBD) in the spike protein is the most variable part of the coronavirus family genome. SARS-CoV-2 seems to have an RBD that binds with high affinity to ACE2 from humans, and other species with high receptor homology. This RBD has six key amino acid residues.
Evidence #2
The second notable feature of SARS-CoV-2 is a polybasic cleavage site at the junction of S1 and S2, the two subunits of the spike. This allows effective cleavage by furin and other proteases and has a role in determining viral infectivity and host range. Insertion of proline to this site and subsequent addition of O-linked glycans are unique to SARS-CoV-2.
Keeping these in mind, we have:
Theory #1
Natural selection in animal before zoonotic transfer
As many early cases of COVID-19 were linked to the Huanan market in Wuhan, it is possible that an animal source was present at this location.
Given the similarity of SARS-CoV-2 to bat SARS-CoV-like coronaviruses, it is likely that bats serve as reservoir hosts for its progenitor. This "bat virus" or more formally, RaTG13 is nearly 96% identical to SARS-CoV-2. Its spike diverges in the RBD, which suggests that it may not bind efficiently to human ACE2.
Malayan pangolins illegally imported into Guangdong province contain coronaviruses similar to SARS-CoV-2. Some "pangolin coronavirus" exhibit strong similarity to SARS-CoV-2 in the RBD, including all six key RBD residues. This clearly shows that the SARS-CoV-2 spike protein optimised for binding to human-like ACE2 is the result of natural selection.
Neither the bat nor the pangolin coronavirus, however, has polybasic cleavage sites. This means, no animal coronavirus has been identified that is sufficiently similar to be the direct progenitor of SARS-CoV-2. That said, the diversity of coronaviruses in bats and other species is massively undersampled. Mutations, insertions and deletions can occur near the S1–S2 junction of coronaviruses, which shows that the polybasic cleavage site can arise by a natural evolutionary process. This perfectly sets us up for our next theory.
Theory #2
Natural selection in human after zoonotic transfer
It is possible that a progenitor of SARS-CoV-2 jumped into humans to acquire the genomic features described above through adaptation, during undetected human-to-human transmission. Once acquired, these adaptations would enable the pandemic to take off.
All SARS-CoV-2 genomes sequenced so far have the genomic features described above and are thus derived from a common ancestor that had them too. The "pangolin coronavirus" has an RBD very similar to that of SARS-CoV-2, by the process of natural selection. From this, we can infer the same happened with the virus that jumped to humans. So we can say, with some degree of confidence, the insertion of polybasic cleavage site occured during human-to-human transmission.
From what we know the first case of COVID-19 has been traced back to November 2019. This presumes a period of unrecognised human-to-human transmission, between the initial zoonotic event and the acquisition of the polybasic cleavage site.
Theory #3
Lab experiment gone wrong
Basic research involving passage of bat SARS-CoV-like coronaviruses in cell culture and animal models has been ongoing for many years in biosafety level 2 laboratories across the world, and there are documented instances of laboratory escapes of SARS-CoV. In theory, it is possible that SARS-CoV-2 acquired RBD mutations during adaptation to passage in cell culture.
Having said that, the "pangolin coronavirus" with nearly identical RBDs, provides a much stronger explanation of how SARS-CoV-2 acquired these via recombination or mutation. The high-affinity binding of the SARS-CoV-2 spike protein to human ACE2 is most likely the result of natural selection on a human or human-like ACE2.
The acquisition of both the polybasic cleavage site and predicted O-linked glycans also argues against culture-based scenarios. New polygenic cleavage sites have only been observed after prolonged in-vivo passage whereas generating O-linked glycans likely involves an immune system.
Furthermore, if genetic manipulation had been performed, one of the several reverse-genetic systems available for coronaviruses would probably have been used. However, the genetic data irrefutably show that SARS-CoV-2 is not derived from any previously used virus backbone.
These are strong arguments that SARS-CoV-2 is not the product of purposeful manipulation.
Conclusion
Theory #2 seems most likely, given the information currently available, but more scientific data could swing the balance of evidence to favour one hypothesis over another. What's important is to further study the possible origins, not just for understanding the current zoonotic pandemic but also to prevent the potential future ones.
References
1. 'The proximal origin of SARS-CoV-2' by Andersen et al: www.nature.com/articles/s41591-020-0820-9
2. 'A pneumonia outbreak associated with a new coronavirus of probable bat origin' by Zhou et al: www.nature.com/articles/s41586-020-2012-7
3. 'A new coronavirus associated with human respiratory disease in China' by Wu et al: www.nature.com/articles/s41586-020-2008-3
- Ashish Singh
Friday, April 24, 2020
Coronary artery anatomy mnemonic and video for visualization
Let's learn about the coronary artery anatomy today (and never forget it!)
Watch the video. Text and images below.
Watch the video. Text and images below.
Coronary artery dominance and EKG changes
Hello, hello!
Coronary arterial dominance is defined by the vessel which gives rise to the posterior descending artery (PDA).
Coronary arterial dominance is defined by the vessel which gives rise to the posterior descending artery (PDA).
Funnel Plot
-also called as Begg’s plot
-type of scatter plot
-used to examine biases in meta-analyses
An ideal funnel plot is symmetric.
If no biases, 95% of studies lie within the triangle.
-type of scatter plot
-used to examine biases in meta-analyses
An ideal funnel plot is symmetric.
If no biases, 95% of studies lie within the triangle.
Wednesday, April 22, 2020
Thursday, April 16, 2020
Thioamides in pregnancy
Hello
Propylthiouracil is a pro. It always comes first (used in first trimester of pregnancy).
Methimazole causes Malformations in the embryo (teratogenic).
There are two M's in MethiMazole. This drug is used in second (and third trimester of pregnancy).
Propylthiouracil piles up, causing liver toxicity, thus limiting its use.
Hope it helps
- Jaskunwar Singh
Propylthiouracil is a pro. It always comes first (used in first trimester of pregnancy).
Methimazole causes Malformations in the embryo (teratogenic).
There are two M's in MethiMazole. This drug is used in second (and third trimester of pregnancy).
Propylthiouracil piles up, causing liver toxicity, thus limiting its use.
Hope it helps
- Jaskunwar Singh
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