Fields Of Biological Sciences Research Articles

Current Bioinformatics Tools to Optimize CRISPR/Cas9 Experiments to Reduce Off-Target Effects.

The CRISPR-Cas system has evolved into a cutting-edge technology that has transformed the field of biological sciences through precise genetic manipulation. CRISPR/Cas9 nuclease is evolving into a revolutionizing method to edit any gene of any species with desirable outcomes. The swift advancement of CRISPR-Cas technology is reflected in an ever-expanding ecosystem of bioinformatics tools designed to make CRISPR/Cas9 experiments easier. To assist researchers with efficient guide RNA designs with fewer off-target effects, nuclease target site selection, and experimental validation, bioinformaticians have built and developed a comprehensive set of tools. In this article, we will review the various computational tools available for the assessment of off-target effects, as well as the quantification of nuclease activity and specificity, including web-based search tools and experimental methods, and we will describe how these tools can be optimized for gene knock-out (KO) and gene knock-in (KI) for model organisms. We also discuss future directions in precision genome editing and its applications, as well as challenges in target selection, particularly in predicting off-target effects.

Correlative microscopy and block-face imaging (CoMBI): a 3D imaging method with wide applicability in the field of biological science.

Correlative microscopy and block-face imaging (CoMBI) is an imaging method, which is characterized by the ability to obtain both serial block-face images as a 3-dimentional (3D) dataset and sections for 2-dimentional (2D) light microscopic analysis. These 3D and 2D morphological data can be correlated with each other to facilitate data interpretation. CoMBI is an easy-to-install and low-cost 3D imaging method since its system can be assembled by the researcher using a regular microtome, consumer digital camera, and some self-made devices, and its installation and instruction manuals are open-source. After the first release of CoMBI method from our laboratory, CoMBI systems have been installed in more than a dozen laboratories and are used for 3D analysis of various biological specimens. Typical application of CoMBI is 3D anatomical analysis using the natural color and contrast of the specimen. We have been using CoMBI for analyzing human brain to obtain the fine 3D anatomy as a reference to determine the causes of neurological diseases and to improve the effectiveness of surgery. Recently, we have been using CoMBI for detecting the colors of chromogens, which are used for labeling specific molecules. Mouse embryos colored with X-gal, a conventional chromogen for detecting LacZ products, were imaged using CoMBI, and the 3D distribution of X-gal was successfully visualized. Thus, CoMBI can now be used for many purposes, including 3D anatomical analysis, 2D microscopy using sections, and 3D distribution of specific molecules. These suggest that CoMBI should be more widely used in the field of biological research.

RASCON: Future of Rehabilitation and Allied Health Sciences in Pakistan

Healthcare is a crucial aspect in our society. With the improvement in technology and education, more people are opting for this profession. In recent decades, rehabilitation, allied health sciences and emerging fields in biological sciences have received considerable attention at both international and national platforms. The Superior University has the largest and highly qualified faculty in the key disciplines of Rehabilitation & Allied Health Sciences. In Current Senior, Faculty of Allied Health has Organized RASCON, the first ever international conference, on 9th-11th December 2022 under the theme “Interdisciplinary approaches to create socio-economic impact through research, innovation and commercialization for healthcare professionals and scientists.” The conference has attributed to augmentation and expansion of research, education and commercialization in these domains. The main objective of this conference is to bring together the health care professionals, Allied Health care professionals, Rehabilitation specialists, Physical Therapists, Speech Language Pathologists, Clinical Psychologists, Clinical Nutritionists, Occupational Therapists, Leading scientists, Researchers, Educationists and allied health care professionals to exchange interdisciplinary approaches to contribute to socio-economic impact to achieve SDGs. It has been planned to provide an intercontinental platform for the sharing of scientific knowledge and hands on training sessions. Similar to other conferences, while the major audience included researchers around the globe, it specifically provided a research knowledge sharing venture for the students, which are going to be our future researchers In this regard, various workshops (26) were conducted on 9th December in collaboration with different hospitals and industries. The main objective of these workshops was to provide hands on training and facilitate students to grab emerging technologies relating to their respective fields. On 10 and 11 December, around 17 International and 56 National keynote speakers attended the conference to present their latest research in their specialized fields. . Our global speakers included professors from USA, Canada, Malaysia, Uk, Iran, China, Germany, Sri Lanka, Australia, Russia, Sweden, Turkey, Thailand and South Korea. Furthermore, participants of this conference also presented scientific posters highlighting the outcomes of their recent projects. Additionally, several talk sessions were also conducted to answer the queries of the students and other participants. This conference was successfully held with the collaboration of National/Multinational industries including food industries, academic institutes and healthcare sectors. The prime knowledge products of RASCON 22 were: research articles, patents, books, research protocols, manuals, policies, advanced technologies in therapeutics, outreach and assessment resources, blogs on socio-economic impacts of research, and finally the entrepreneurial implications of research. Of all the planned undertakings, the greatest impact was seen in societal projects, followed by manuals, advanced therapeutic technologies, blogs, policies and protocols, in ascending order. The manuals provided information and necessary steps for improvements in physiotherapy and rehabilitation. Some key protocols included posture control, communication and swallowing management along with a stepwise diagnosis protocol for dyslexia – a learning disability that has affected more than 700 million people worldwide and 12 million children in Pakistan alone. Correspondingly, the advanced therapeutic technologies included robotic rehabilitation options in the fields of orthotics, cochlear implantation and prosthetics. Major polices focused on the investment and management so as to build a high-quality workforces and services for improving rehabilitation combined with a feedback system via data collection for continual improvement. All of these would ultimately have a strong societal impact in the form of community-based rehabilitation culminating in the better quality of life. Overall it was a fantastic conference, we are grateful to have of thoughtful and effective organization.” The level of expertise and knowledge of the presenters are excellent. In addition, I appreciate their positive attitudes, willingness to explain concepts, clarity of visual aids and handouts, and opportunities to ask questions. The insights I have gained from this one conference far exceed any other conference I have attended to date. Wonderful, insightful, well presented! Excellent sessions, challenging and thought-provoking. I am always impressed with the commitment and efficiency of those who plan this conference.” “I really enjoyed my time at the conference. I am looking forward to next year's conference.”

Antifungal activity of green synthesized selenium nanoparticles and their effect on physiological, biochemical, and antioxidant defense system of mango under mango malformation disease.

Plant extract-based green synthesis of nanoparticles is an emerging class of nanotechnology that has revolutionized the entire field of biological sciences. Green synthesized nanoparticles are used as super-growth promoters and antifungal agents. In this study, selenium nanoparticles (SeNPs) were synthesized using Melia azedarach leaves extract as the main reducing and stabilizing agent and characterized by UV-visible spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray (EDX), and fourier transform infrared spectrometer (FTIR). The green synthesized SeNPs were exogenously applied on Mangifera indica infected with mango malformation disease. The SeNPs at a concentration of 30 μg/mL were found to be the best concentration which enhanced the physiological (chlorophyll and membrane stability index), and biochemical (proline and soluble sugar) parameters. The antioxidant defense system was also explored, and it was reported that green synthesized SeNPs significantly reduced the biotic stress by enhancing enzymatic and non-enzymatic activities. In vitro antifungal activity of SeNPs reported that 300 μg/mL concentration inhibited the Fusarium mangiferae the most. This study is considered the first biocompatible approach to evaluate the potential of green synthesized SeNPs to improve the health of mango malformation-infected plants and effective management strategy to inhibit the growth of F. mangifera.

Cell-loaded hydrogel microspheres based on droplet microfluidics: a review

Droplet microfluidics technology offers refined control over the flows of multiple fluids in micro/nano-scale, enabling fabrication of micro/nano-droplets with precisely adjustable structures and compositions in a high-throughput manner. With the combination of proper hydrogel materials and preparation methods, single or multiple cells can be efficiently encapsulated into hydrogels to produce cell-loaded hydrogel microspheres. The cell-loaded hydrogel microspheres can provide a three-dimensional, relatively independent and controllable microenvironment for cell proliferation and differentiation, which is of great value for three-dimensional cell culture, tissue engineering and regenerative medicine, stem cell research, single cell study and many other biological science fields. In this review, the preparation methods of cell-loaded hydrogel microspheres based on droplet microfluidics and its applications in biomedical field are summarized and future prospects are proposed.

Metabolomics and molecular networking approach for exploring the anti-diabetic activity of medicinal plants.

Metabolomics and molecular networking approaches have expanded rapidly in the field of biological sciences and involve the systematic identification, visualization, and high-throughput characterization of bioactive metabolites in natural products using sophisticated mass spectrometry-based techniques. The popularity of natural products in pharmaceutical therapies has been influenced by medicinal plants with a long history of ethnobotany and a vast collection of bioactive compounds. Here, we selected four medicinal plants Cleistocalyx operculatus, Terminalia chebula, Ficus lacor, and Ficus semicordata, the biochemical characteristics of which remain unclear owing to the inherent complexity of their plant metabolites. In this study, we aimed to evaluate the potential of these aforementioned plant extracts in inhibiting the enzymatic activity of α-amylase and α-glucosidase, respectively, followed by the annotation of secondary metabolites. The methanol extract of Ficus semicordata exhibited the highest α-amylase inhibition with an IC50 of 46.8 ± 1.8 μg mL-1, whereas the water fraction of Terminalia chebula fruits demonstrated the most significant α-glucosidase inhibition with an IC50 value of 1.07 ± 0.01 μg mL-1. The metabolic profiling of plant extracts was analyzed through Liquid Chromatography-Mass Spectrometry (LC-HRMS) of the active fractions, resulting in the annotation of 32 secondary metabolites. Furthermore, we applied the Global Natural Product Social Molecular Networking (GNPS) platform to evaluate the MS/MS data of Terminalia chebula (bark), revealing that there were 205 and 160 individual ion species observed as nodes in the methanol and ethyl acetate fractions, respectively. Twenty-two metabolites were tentatively identified from the network map, of which 11 compounds were unidentified during manual annotation.

ARTIFICIAL INTELLIGENCE AND STOCHASTIC OPTIMIZATION ALGORITHMS FOR THE CHAOTIC DATASETS

Almost every natural process is stochastic due to the basic consequences of nature’s existence and the dynamical behavior of each process that is not stationary but evolves with the passage of time. These stochastic processes not only exist and appear in the fields of biological sciences but are also evident in industrial, agricultural and economical research datasets. Stochastic processes are challenging to model and to solve as well. The stochastic patterns when repeated result into random fractals and are very common in natural processes. These processes are usually simulated with the aid of smart computational and optimization tools. With the progress in the field of artificial intelligence, smart tools are developed that can model the stochastic processes by generalization and genetic optimization. Based on the basic theoretical description of the stochastic optimization algorithms, the stochastic learning tools, stochastic modeling, stochastic approximation and stochastic fractals, a comparative analysis is presented with the aid of the stochastic fractal search, multi-objective stochastic fractal search and pattern search algorithms.

From Sequence Analysis to Application

The field of biological sciences enumerates various important aspects of life forms, some of which remain mysterious to explain or validate. Though there has been a revolutionary advancement of tools and techniques to study different biological phenomena under laboratory conditions, there are significant limitations with implementing each concept in the laboratory. Sometimes, it is practically impossible to simulate the actual environmental conditions of living systems under in-vitro settings, or sometimes the requirements of life are discordant with various analytical techniques, or the study of complex evolutionary processes becomes technically difficult using wet-lab methods. Thus, these experimental challenges confine our boundary to explore the real world of biological systems, which may lead to the acquisition of knowledge with few lacunae. Bioinformatics as a discipline tries to fill those spaces to a major extent using in silico analysis and provides a deeper theoretical foundation and validation of existing biological principles. The importance and potential of bioinformatics have been witnessed in the pandemic times to fight against Covid-19. From developing new drugs and vaccines to crop improvement and space and environment studies, the field of bioinformatics has many prospects. This article aims to provide a bird’s-eye view of different applications and cutting-edge bioinformatics approaches to understand biological systems and the emerging need to integrate this course into the education framework.

GwAAP: A genome-wide amino acid coding-decoding quantitative proteomics system.

Mass spectrometry-based proteomic technology has greatly improved and has been widely applied in various biological science fields. However, proteome-wide accurate quantification of proteins in signaling pathways remains challenging. Here, we report a genome-wide amino acid coding-decoding quantitative proteomic (GwAAP) system to facilitate precise proteome quantification. For each protein, a unique code peptide was assigned and incorporated into the N-terminus of the targeted protein and used for identification and quantification. As a proof of principle, we systematically tagged 40 yeast proteins with codes and employed mass spectrometry to decode. We successfully recovered all 40 code peptides with a large and consistent quantitative dynamic range (CV slope <10%, R2>0.8). We further verified the alteration of the glucose and galactose metabolism pathways in yeast under different carbon source conditions. The GwAAP system could potentially provide a strategy to achieve absolute quantification of the entire yeast proteome without bias.

Network approach to understand biological systems: From single to multilayer networks

Network theory has led to the abstraction of many real-world systems and enabled their modelling as simple networks comprising nodes and edges. In particular, in the field of biological sciences, network theory provides a robust framework to capture the complexity inherent to biological systems. Networks in biology have been modelled at different scales, starting from cells to population levels. These models have provided crucial insights into the evolution, mechanism, and functions of several biological systems. However, most natural and engineered systems are composed of multiple subsystems and layers of connectivity. A multilayer network paradigm has proven useful in understanding such systems. Here, we have briefly introduced the network formalism of modelling biological systems at various levels. This is followed by an introduction to multilayer networks. Multilayer networks have been utilized to model biological systems at multiple scales ranging from protein-protein interactions, transcription and metabolic networks, to ecological networks involving interactions between species. Recent advances in studying the structure and dynamics of such multilayer networks have enabled a better understanding of the complexity in these biological systems. Finally, we discuss the recent advances in studying the structure and dynamics of such multilayered networks followed by the challenges and future prospects.

MALDI-TOF MS: application in diagnosis, dereplication, biomolecule profiling and microbial ecology

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has revolutionized scientific research over the past few decades and has provided a unique platform in ongoing technological developments. Undoubtedly, there has been a bloom chiefly in the field of biological sciences with this emerging technology, and has enabled researchers to generate critical data in the field of disease diagnoses, drug development, dereplication. It has received well acceptance in the field of microbial identification even at strain level, as well as diversified field like biomolecule profiling (proteomics and lipidomics) has evolved tremendously. Additionally, this approach has received a lot more attention over conventional technologies due to its high throughput, speed, and cost effectiveness. This review aims to provide a detailed insight regarding the application of MALDI-TOF MS in the context of medicine, biomolecule profiling, dereplication, and microbial ecology. In general, the expansion in the application of this technology and new advancements it has made in the field of science and technology has been highlighted.

Improving Printability of Digital-Light-Processing 3D Bioprinting via Photoabsorber Pigment Adjustment.

Digital-light-processing (DLP) three-dimensional (3D) bioprinting, which has a rapid printing speed and high precision, requires optimized biomaterial ink to ensure photocrosslinking for successful printing. However, optimization studies on DLP bioprinting have yet to sufficiently explore the measurement of light exposure energy and biomaterial ink absorbance controls to improve the printability. In this study, we synchronized the light wavelength of the projection base printer with the absorption wavelength of the biomaterial ink. In this paper, we provide a stepwise explanation of the challenges associated with unsynchronized absorption wavelengths and provide appropriate examples. In addition to biomaterial ink wavelength synchronization, we introduce photorheological measurements, which can provide optimized light exposure conditions. The photorheological measurements provide precise numerical data on light exposure time and, therefore, are an effective alternative to the expendable and inaccurate conventional measurement methods for light exposure energy. Using both photorheological measurements and bioink wavelength synchronization, we identified essential printability optimization conditions for DLP bioprinting that can be applied to various fields of biological sciences.

A Reliable Approach for Revealing Molecular Targets in Secondary Ion Mass Spectrometry.

Nano secondary ion mass spectrometry (nanoSIMS) imaging is a rapidly growing field in biological sciences, which enables investigators to describe the chemical composition of cells and tissues with high resolution. One of the major challenges of nanoSIMS is to identify specific molecules or organelles, as these are not immediately recognizable in nanoSIMS and need to be revealed by SIMS-compatible probes. Few laboratories have generated such probes, and none are commercially available. To address this, we performed a systematic study of probes initially developed for electron microscopy. Relying on nanoscale SIMS, we found that antibodies coupled to 6 nm gold particles are surprisingly efficient in terms of labeling specificity while offering a reliable detection threshold. These tools enabled accurate visualization and sample analysis and were easily employed in correlating SIMS with other imaging approaches, such as fluorescence microscopy. We conclude that antibodies conjugated to moderately sized gold particles are promising tools for SIMS imaging.

Recombinase polymerase amplification in the molecular diagnosis of microbiological targets and its applications.

Since the introduction of the polymerase chain reaction (PCR) technique in 1983, nucleic acid amplification has permeated all fields of biological science, particularly clinical research. Despite its importance, PCR has been restricted to specialized centers and its use in laboratories with few resources is limited. In recent decades, there has been a notable increase in the development of new isothermal technologies for molecular diagnosis with the hope of overcoming the traditional limitations of the laboratory. Among these technologies, recombinase polymerase amplification (RPA) has a wide application potential because it does not require thermocyclers and has high sensitivity, specificity, simplicity, and detection speed. This technique has been used for DNA and RNA amplification in various pathogenic organisms such as viruses, bacteria, and parasites. In addition, RPA has been successfully implemented in different detection strategies, making it a promising alternative for performing diagnoses in environments with scarce resources and a high burden of infectious diseases. In this study, we present a review of the use of RPA in clinical settings and its implementation in various research areas.

ESTABLISHMENT AND PARTIAL CHARACTERIZATION OF FETAL FIBROBLAST CELLS OF BLACK BENGAL BREED

Animal cells are a valuable genetic resource and become the major tool for routine investigation of biological research and regenerative medicine. The somatic cells are as valuable as germ cells and an attractive resource for conserving animal genetic materials. The present study was to establish and characterized the caprine fetal fibroblast cell culture of Back Bengal goat's genetic resources; the breed is one of the highly prolific breeds among Indian breeds of goat and famous for meat and skin. The fetal fibroblast cells of Back Bengal goat breed at the age of 1-2 month was cultured. The cells were highly prolific, able to identify by molecular markers, with a population doubling time (PDT) approximately 73.6 hours and could be useful for gene expression study. These cells will be valuable assets for other genomics, post genomics, somatic cloning, and other fields of biological sciences in the future.

Au nanorods-TiO2 photonic crystal plasmonic-photonic hybrid sensor for label-free detection and identification of DNA molecules with single nucleotide polymorphisms

Highly sensitive optical sensors for label-free detection of DNA must have high usability and wide applicability for achieving advances in the medical and biological science fields. However, current sensors do not simultaneously satisfy these criteria. Here, we developed a simple, cost-effective, and sensitive plasmonic–photonic hybrid sensor using gold nanorods as nanoantennae and a photonic crystal slab (PCS) as a microcavity. In this report, we performed simple microscopic detection and identification of DNA molecules with Alzheimer’s disease-associated single nucleotide polymorphisms, which is an important indicator for early diagnosis. The PCS was fabricated using nanoimprint lithography; the gold nanorods were controllably coupled with the PCS. The device sensitivity was experimentally assessed by controlling the plasmonic–photonic coupling resonance, which was enabled by tuning the PCS-coupled AuNR amounts. The sensitivity was also investigated by theoretical modeling of the hybrid sensor, based on the temporal coupled mode theory (TCMT). The TCMT-based theoretical model well explained the experimental results. In a demonstration of ultrasensitive detection of DNA, the identification of single nucleotide mismatched DNA molecules (~1 pM) and detection of several DNA molecules (~1000), with a limit of detection of 5.9 aM, were successfully achieved. This sensor may thus have wide applications for DNA analysis.

Synthesis, Characterization, Properties, and Novel Applications of Fluorescent Nanodiamonds.

In the last few years, fluorescent nanodiamonds (FNDs) have been developed significantly as a new member in the nanocarbon family. The surface of FNDs is embedded with some crystallographic defects containing color centres which surmount the properties of other fluorochromes including up conversion and down conversion nanoparticles, quantum dots, nano tubes, fullerenes, organic dyes, silica etc. Some of the intriguing properties like inevitable photostability, inherent bio-compatibility, outstanding optical and robust mechanical properties, excellent magnetic field, and electric field sensing potentiality make FNDs appealing to some benevolent applications in numerous fields like bio-imaging, delivering drugs, fighting cancer, spin electronics, imaging of magnetic structure at nanoscale and as promising nanometric temperature sensor. The structure of FNDs has certain point defects on the surface among which negatively charged nitrogen vacancy centre (NV-) is the most investigated color centre. The production of NV- fluorescence nanodiamonds is the most challenging task as substitution of carbon atoms is required to create vacancies by causing irradiation from an electron beam which is followed by high temperature annealing. Thus, this review points out the relative advantages of FNDs containing negatively charged nitrogen vacancy centres produced from HPHT method or CVD method with those nanodiamonds produced through detonation process or pulsed laser ablation (PLA) method. The steps involved in the fabrication of FNDs are described along with the major challenges and struggles underwent during the process in this review. This review also summarizes the recent developments made in the functionalization and applications predominantly made in the field of biological science and it is understood that depending on the defect color centres they can exhibit different emitted wavelengths ranging from UV-visible to near infrared with broad or narrow bandwidths. This review also highlights some of the fluorescent NDs that emit stable and strong red or green photoluminescence from the defect centers of NV- which are implanted in the crystal lattice. This critical and extensive review will be useful for the further progress in this futuristic field of FNDs.

Population Size Estimation using Zero-truncated Poisson Regression with Measurement Error.

Population size estimation is an important research field in biological sciences. In practice, covariates are often measured upon capture on individuals sampled from the population. However, some biological measurements, such as body weight may vary over time within a subject's capture history. This can be treated as a population size estimation problem in the presence of covariate measurement error. We show that if the unobserved true covariate and measurement error are both normally distributed, then a naïve estimator without taking into account measurement error will under-estimate the population size. We then develop new methods to correct for the effect of measurement errors. In particular, we present a conditional score and a nonparametric corrected score approach that are both consistent for population size estimation. Importantly, the proposed approaches do not require the distribution assumption on the true covariates, furthermore the latter does not require normality assumptions on the measurement errors. This is highly relevant in biological applications, as the distribution of covariates is often non-normal or unknown. We investigate finite sample performance of the new estimators via extensive simulated studies. The methods are applied to real data from a capture-recapture study.

KLASTERISASI MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING DENGAN MENGGUNAKAN INDIKATOR TOTAL DEBT TO TOTAL EQUITY RATIO, CURRENT RATIO DAN RETURN ON ASSETS UNTUK MENGETAHUI DAMPAK COVID-19 PADA 10 INDUSTRI DI BURSA EFEK INDONESIA

Clustering data is a data grouping method. This is a part of Data Mining, which is pattern extraction that interested of the big data. Clustering usually use in business intelligence, image pattern recognition, web search, field of biological sciences, and for security. Clustering is a process grouping data into clusters so that data in one cluster have the same maximum. Object in one cluster have the same characteristic between each others and different with other cluster. This research using K-means Clustering to grouping company in some industries based on available data in yahoo finance. Grouping aim to know about which good company to invest in during this pandemic. Data source financial statement in 2020 that available in yahoo finance. Analysis method using K-means clustering with software SPSS version 20. The result is 3 clusters, which is cluster 1 severely affected by covid, cluster 2 mildly affected by covid and cluster 3 moderately affected by covid. Result of analysis there is 7 companies in cluster 1, 1 companies in cluster 2, and 2 companies in cluster 3. Based on analysis result using K-means Clustering then suggested to invest on company that include in cluster 2 is Unilever Indonesia Tbk.

I’ve Been Misgendered So Many Times: Comparing the Experiences of Chronic Misgendering among Transgender Graduate Students in the Social and Natural Sciences

Chronic misgendering is the process of being repeatedly misgendered (referred to as another gender) after informing an individual of gender pronouns (e.g., “she,” “he,” “they”). Chronic misgendering is symbolic of larger institutional and disciplinary adherence to a paradigm that privileges cisgender people, referred to as a gender essentialist paradigm. In order to understand which disciplines in higher education have more pervasive chronic misgendering, we analyze results from the National Survey of Transgender Graduate Students (n = 245). Graduate students in the natural sciences experience more chronic misgendering compared to graduate students in the social sciences. Those in health and biological science fields (in and closely related to medicine) reported the highest level of chronic misgendering, accounting for the majority of all chronic misgendering in the natural sciences. We argue that not only do these incidents negatively impact transgender graduate students, but they also reflect and reproduce field‐specific expectations for what is considered acceptable misgendering practice in post‐graduate professional environments, such as community health and medicine. As such, chronic misgendering in graduate school functions as unofficial curricula and thus, training for workplace cultures that, to different degrees, devalue transgender people and contribute to structural inequalities.