Bibliographic record
Abstract
I was 10 years old when my grandfather, who was then a professor of pathology at a medical university in the Soviet Union, gave me my first microscope. It was a refurbished 25-year-old light microscope. I have many fond memories of my beginnings in microscopy. I was thrilled to discover the dynamic and incredibly diverse world of microorganisms revealed to me in the drop of puddle water. As I watched that swarming assemblage of ciliates, flagellates, and amoebas on my first slides, I became fascinated with their complex, intricate organization. I remember having a strong desire to capture the images that unraveled before me. I began to fill pages of notebooks with sketches of different tiny critters that captured my imagination. Now, many years later, as a medical student at the University of Alabama in Birmingham, I continue to draw from the set of microscopy skills I learned as a child to study the biology of cancerous cells. Since my first experiences with microscopy, I have advanced to using cutting-edge imaging techniques such as fluorescent confocal microscopy as well as transmission and scanning electron microscopy (SEM). Learning each of these techniques is often a long and tedious process involving days of work in the laboratory followed by hours of searching for the desired frame under the microscope. However, there is nothing more gratifying than obtaining an image that is not only scientifically informative but also reveals the beauty and intricacy of nature in unexpected ways. One of the most stunning modes of visualizing tissue is SEM, which uses the high-energy beam of electrons to probe the surface of specimens yielding unique three-dimensional images of surface topography magnified up to 2,000,000 times. The microphotograph featured on this month's cover represents one of my first attempts at mastering the skill of SEM as a graduate student at Clemson University. During that time, I studied the molecular basis of pathogenicity of a human intestinal parasite, Entamoeba histolytica. This parasite afflicts more than 500 million people worldwide, causing significant morbidity and mortality from diarrheal disease. For example, in Bangladesh, 1 in every 30 children dies of diarrhea or dysentery before the age of 5. The disease can be contracted by ingestion of contaminated food or water that contains E. histolytica spores. Once in the human intestine, this parasite invades the wall of the intestine, causing tissue destruction and bloody diarrhea. The invasiveness of E. histolytica depends on its ability to engulf human red blood cells, bacteria, and other contents of human large intestines. To understand the molecular basis of this organism's ability to invade human intestines, I used a variety of imaging techniques, including SEM, to visualize this process. After several hours of searching for the perfect frame of E. histolytica taking up a human red blood cell, I accidentally stumbled on an unexpected frame of an amoeba engulfing a ball of fungus, which was likely a mere contaminant of the growth media for these parasites. In the background of dormant, sleepy-looking parasites, I captured a single amoeba whose usually smooth outer surface appeared like the gaping mouth of a hungry predator frozen in time while devouring its prey. I was fascinated by this struggle between the two microscopic creatures. To enhance this alien and almost surrealistic microscopic landscape, I used a computerized pseudocoloring technique. When the process was completed, the image before me came to life. Instead of just depicting a certain feeding behavior of this parasite, it became a vibrant story of the interaction between the two inhabitants of a fascinating world of microbes hidden from our naked eye.Figure: The Last SupperExperimenting with different ways of capturing and viewing microscopic photographs has not only become my hobby but has also provided me with a unique way to communicate science to the general public. I have always found it quite challenging to describe my work as a molecular scientist and convey my passion for microbiology to people outside of this field. The art of microphotography now helps me to share my love for molecular science with my colleagues, students, and even family and friends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.350 | 0.210 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".