Life Sciences Research Articles

Life scientists’ experience with posting preprints during the COVID-19 pandemic

AbstractIn the COVID-19 pandemic, it was much more critical for many life science researchers to rapidly disseminate research results—so they used preprints as upstream publication opportunities. This was rather new to the life sciences where preprint servers had only appeared as early as 2013. With a mixed-methods-study we examined this development and investigated whether preprint posting is a temporary phenomenon or the beginning of a cultural shift in publishing behavior in the life sciences. First, we conducted a survey of researchers who have posted COVID-19 related preprints. We investigated experiences with posting preprints during the COVID-19 pandemic, motivations for and concerns about posting preprints, the role of research institutions or funders, and the future of preprint publishing. Answers were grouped to compare differences between respondents’ gender, career stage, region of origin (global south or global north) and experience with posting preprints before and during the COVID-19 pandemic. We further analyzed eight popular preprint repositories regarding the number of posted preprints and preprint characteristics, such as the number of authors and citations. Interestingly, survey and preprint server analysis have presented different, if not contradicting results: While the majority of surveyed researchers was willing to continue posting preprints, the numbers of preprints published, especially on servers for the life sciences, have stagnated or declined. Also, while certain preprints garnered substantial citations during the COVID-19 pandemic, this has not resulted in a significant shift in researchers’ publishing behavior, and the posting of preprints has not become a routine. We concluded that the sustainability of preprint publishing practices is more strongly influenced by disciplinary norms and practices than by external shocks as the COVID-19 pandemic.

The Disruptive Influence of Generative AI in Life Science and Healthcare

The Disruptive Influence of Generative AI in Life Science and Healthcare

Experiences of Using a Real Compound Microscope and a Virtual Compound Microscope in Understanding Plant Tissues by First-year Life Science Students at a Tertiary Institution in South Africa

During the COVID-19 outbreak, the majority of tertiary institutions preferred virtual laboratories to face-to-face laboratories which posed a challenge to many students. The objective of the study was to examine students’ experiences at a higher institution using both a physical and virtual microscope. This qualitative study involved ten randomly chosen first-year Life Science students. They had to prepare and view onion cells under a real microscope, while the second assignment required them to observe onion cell preparation in a virtual laboratory. Students completed a questionnaire containing open-ended questions. The results were analyzed using themes, and the study found that pupils prefer physical microscopes to virtual microscopes. This is because students interact with the equipment in the laboratory, which encourages intellectual interests, knowledge sharing, and the development of social connections. Students can manipulate actual objects, manually focus microscopes, and interact in real time with microscopic worlds. This hands-on expertise is critical for instructing students and researchers on laboratory practices. Real microscopes are versatile pieces of equipment that can be utilized for a wide range of applications, from basic instruction to advanced research. Although virtual microscopes have advanced in terms of image quality, real microscopes provide better resolution and clarity. The study will make a substantial addition to university laboratory pedagogies and expose students to scientific investigations, given the majority of students come from rural schools with limited laboratory equipment such as microscopes. Keywords: Physical Microscope, Virtual Microscope, Experiences, Intellectual Interest, Social Interactions

Abstract LB236: Metabolically armed CD19 CAR-T cells for safe and effective treatment of relapsed or refractory CD19+ B cell hematological malignancies at extremely low doses

Abstract Background: The undeniable success of CD19 CAR-T cell therapy in managing relapsed or refractory (r/r) B-cell hematological malignancies, such as diffuse large B-cell lymphoma (DLBCL) and B-cell acute lymphoblastic leukemia (B-ALL), underscores its significance. Despite the favorable complete remission (CR) rates of around 50% for r/r DLBCL and up to 90% for r/r B-ALL, a notable challenge persists as more than half of patients experiencing CR after CD19 CAR-T cells treatment encounter relapse within a year, attributed to inadequate CAR-T persistence in both indications. Urgent attention is required to devise innovative strategies to overcome these challenges. Recently, we developed metabolically armed CD19 CAR-T cells expressing IL-10 can enhance OXPHOS metabolism, proliferation, and persistence of CAR-T cells in vivo. Significantly, these IL-10-expressing CAR-T cells trigger stem-like memory responses in various lymphoid organs, leading to robust tumor eradication and durable protection. Motivated by these promising findings, an open-label, single arm, investigator-initiated trial (IIT, ClinicalTrials.gov identifier: NCT05715606, NCT05747157) was conducted to assess the safety and efficacy of newly developed metabolically armed IL-10-expressing CD19 CAR-T (product name: Meta10-19 infusion) for treating patients with r/r DLBCL or r/r B-ALL. Method: From Feb 2023 to Dec 2023, this IIT clinical trial evaluated Meta10-19 infusion in adult patients with r/r DLBCL or r/r B-ALL. The patients were recruited and treated at the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China. Patients in 3 dose cohorts (2.0 × 104, 5.0 × 104, or 1.0 × 105 CAR-T cells/kg, corresponding to 1%, 2.5%, or 5% doses of commercialized CAR-T infusion) underwent lymphodepleting chemotherapy with cyclophosphamide and fludarabine before Meta10-19 infusion. The primary endpoint was the incidence of dose-limiting toxicities. The secondary endpoint was the response and survival. Results: The treatment with Meta10-19 infusion has successfully induced CR in all 12 cases of r/r DLBCL (n=6) or r/r B-ALL (n=6), as evaluated by PET-CT scan, NMR spectroscopy or minimal residual disease assessment of bone marrow. No server cytokine storm syndrome or neurotoxicity was observed. All treated patients achieved CR at 3 months post treatment, with the first patient maintaining continuous remission up to 9 months until now. Notably, these metabolically armed CD19 CAR-T cells demonstrated efficacy even for patients with bulky mass (≥7.5 cm) of DLBCL at extremely low infusion dose (1~5% doses of commercialized CAR-T). Conclusion: Preliminary findings suggest that Meta10-19 infusion exhibits promising breakthrough efficacy and safety profile, and active enrollment is ongoing to validate the initial results. Citation Format: Yugang Guo, Qianwen Xu, Lei Xue, Erling Chen, Chonglin Liu, Min Gao, Youjia Li, Jingjing Ren, Jinping Li, Li Tang, Xingbing Wang. Metabolically armed CD19 CAR-T cells for safe and effective treatment of relapsed or refractory CD19+ B cell hematological malignancies at extremely low doses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB236.

Research Ethics Committee and Integrity Board Members’ Collaborative Decision Making in Cases in a Training Setting

AbstractThis research focuses on how research ethics committee and integrity board members discuss and decide on solutions to case scenarios that involve a dimension of research ethics or integrity in collaborative settings. The cases involved issues around authorship, conflict of interest, disregard of good scientific practice and ethics review, and research with vulnerable populations (children and neonates). The cases were set in a university, a hospital, or a research institute. In the research, we used a deductive qualitative approach with thematic analysis. Twenty-seven research ethics committee and research integrity board members from 16 European countries and one country outside Europe participated. Participants represented natural and life sciences, social sciences, and humanities. They worked on cases involving ethical/integrity issues in six different constellations. Results show that experts apply key elements of ethical decision making, namely identification of ethical issues, stakeholders, guidelines, solutions, and own positionality, in dealing collaboratively with ethics/ integrity problems, and the nature of the application depends on the complexity of the case. Understanding how individuals knowledgeable in research ethics and integrity, in this case, individuals serving on research ethics committees and integrity boards, approach ethical/ moral issues can help to identify strategies that may be useful in the development of research ethics and integrity training for junior researchers who may benefit from learning professional strategies.

Interactions between bacteria and microalgae in microalgal-bacterial symbiotic wastewater treatment systems: mechanisms and influencing factors

Microalgal-bacterial symbiotic wastewater treatment systems (MBSWTSs) have received widespread attention due to their capacity to achieve high pollutant removal efficiency during wastewater treatment, with low energy consumption requirements, efficient carbon sequestration, and wastewater resource utilization. This paper provides an overview of the treatment performance and current research status of MBSWTSs, including a detailed summary of the mechanisms of nitrogen, phosphorus, and carbon removal by MBSWTSs and the interactions between bacteria and microalgae. In particular, this review focuses on the influence of operational parameters on the regulation of the symbiotic system, such as the microalgal:bacterial ratio, N:P ratio, external carbon source, dissolved oxygen concentration, aeration mode, and light availability. Among these factors, the microalgal:bacterial ratio, carbon source, and light availability have an important influence on microalgal-bacterial competition, as well as the trophic mode of the system, biomass production, and the capacity for the process to be practically applied on a large scale. MBSWTSs still have some challenging aspects that have hindered their development and application, such as the unknown mechanism of microalgal-bacterial co-metabolism, limited previous practical applications, algal contamination, and harvesting difficulties. To overcome these challenges, future research requires a multidisciplinary approach, incorporating life sciences, material science, and other disciplines. Comprehensive research should be conducted on the metabolic mechanisms of MBSWTSs, the optimization of process performance and waste resource utilization, providing a theoretical and practical foundation for the practical application of MBSWTSs.

Rethinking 13C-metabolic flux analysis – The Bayesian way of flux inference

Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

Neutralization of EG.5, EG.5.1, BA.2.86, and JN.1 by antisera from dimeric receptor-binding domain subunit vaccines and 41 human monoclonal antibodies

The recently circulating Omicron variants BA.2.86 and JN.1 were identified with more than 30 amino acid changes on the spike protein compared to BA.2 or XBB.1.5. This study aimed to comprehensively assess the immune escape potential of BA.2.86, JN.1, EG.5, and EG.5.1. We collected human and murine sera to evaluate serological neutralization activities. The participants received three doses of coronavirus disease 2019 (COVID-19) vaccines or a booster dose of the ZF2022-A vaccine (Delta-BA.5 receptor-binding domain [RBD]-heterodimer immunogen) or experienced a breakthrough infection (BTI). The ZF2202-A vaccine is under clinical trial study (ClinicalTrials.gov: NCT05850507). BALB/c mice were vaccinated with a panel of severe acute respiratory syndrome coronavirus 2 RBD-dimer proteins. The antibody evasion properties of these variants were analyzed with 41 representative human monoclonal antibodies targeting the eight RBD epitopes. We found that BA.2.86 had less neutralization evasion than EG.5 and EG.5.1 in humans. The ZF2202-A booster induced significantly higher neutralizing titers than BTI. Furthermore, BA.2.86 and JN.1 exhibited stronger antibody evasion than EG.5 and EG.5.1 on RBD-4 and RBD-5 epitopes. Compared to BA.2.86, JN.1 further lost the ability to bind to several RBD-1 monoclonal antibodies and displayed further immune escape. Our data showed that the currently dominating sub-variant, JN.1, showed increased immune evasion compared to BA.2.86 and EG.5.1, which is highly concerning. This study provides a timely risk assessment of the interested sub-variants and the basis for updating COVID-19 vaccines. This work was funded by the National Key R&D Program of China, the National Natural Science Foundation of China, the Beijing Life Science Academy, the Bill& Melinda Gates Foundation, and the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF).

Sonicated polyethylene terephthalate nano- and micro-plastic-induced inflammation, oxidative stress, and autophagy in vitro

The environmental presence of nano- and micro-plastic particles (NMPs) is suspected to have a negative impact on human health. Environmental NMPs are difficult to sample and use in life science research, while commercially available plastic particles are too morphologically uniform. Additionally, this NMPs exposure exhibited biological effects, including cell internalization, oxidative stress, inflammation, cellular adaptation, and genotoxicity. Therefore, developing new methods for producing heterogenous NMPs as observed in the environment is important as reference materials for research. Thus, we aimed to generate and characterize NMPs suspensions using a modified ultrasonic protocol and to investigate their biological effects after exposure to different human cell lines. To this end, we produced polyethylene terephthalate (PET) NMPs suspensions and characterized the particles by dynamic light scattering and scanning electron microscopy. Ultrasound treatment induced polymer degradation into smaller and heterogeneous PET NMPs shape fragments with similar surface chemistry before and after treatment. A polydisperse suspension of PET NMPs with 781 nm in average size and negative surface charge was generated. Then, the PET NMPs were cultured with two human cell lines, A549 (lung) and HaCaT (skin), addressing inhalation and topical exposure routes. Both cell lines interacted with and have taken up PET NMPs as quantified via cellular granularity assay. A549 but not HaCaT cell metabolism, viability, and cell death were affected by PET NMPs. In HaCaT keratinocytes, large PET NMPs provoked genotoxic effects. In both cell lines, PET NMPs exposure affected oxidative stress, cytokine release, and cell morphology, independently of concentration, which we could relate mechanistically to Nrf2 and autophagy activation. Collectively, we present a new PET NMP generation model suitable for studying the environmental and biological consequences of exposure to this polymer.

Insights into interaction of triazine herbicides with three kinds of different alkyl groups (simetryne, ametryn and terbutryn) with human serum albumin via multi-spectral analysis

In this study, the interaction of triazine herbicides with three kinds of different alkyl groups (simetryne, ametryn and terbutryn) with human serum albumin (HSA) are investigated through UV–vis, fluorescence, and circular dichroism (CD) spectra. The mechanisms on the fluorescence quenching of HSA initiated by triazine herbicides are obtained using Stern-Volmer, Lineweaver-Burk and Double logarithm equations. The quenching rate constant (Kq), Stern-Volmer quenching constant (Ksv), binding constant (KA), thermodynamic parameters such as enthalpy change (∆H), entropy change (∆S) and Gibbs free energy (∆G) and number of binding site (n) are calculated and compared. The variations in the microenvironment of amino acid residues are studied by synchronous fluorescence spectroscopy. The binding sites and subdomains are identified using warfarin and ibuprofen as site probes. The conformational changes of HSA are measured using CD spectra. The results reveal that the triazine herbicides with different alkyl groups can interact with HSA by static quenching. The combination of the three herbicides and HSA are equally proportional, and the binding processes are spontaneous. Hydrophobic interaction forces play important roles in simetryne-HSA and ametryn-HSA, while the interaction of terbutryn-HSA is Van der Waals forces and hydrogen bonding. Moreover, the three herbicides can bind to HSA at site I (sub-domain IIA) more than site II (subdomain IIIA), and combine with tryptophan (Trp) more easily than tyrosine (Tyr) residues, respectively. By comparison, the order of interaction strength is terbutryn-HSA > ametryn-HSA > simetryne-HSA. Terbutryn can destroy the secondary structure of HSA more than simetryne and ametryn, and the potential toxicity of terbutryn is higher. It is expected that the interactions of triazine herbicides with HSA via multi-spectral analysis can offer some valuable information for studying the toxicity and the harm of triazine herbicides on human health at molecular level in life science.

Packaging and containerization of computational methods.

Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.

Molecular Profiling and the Impact of Treatment on Outcomes in Adenoid Cystic Carcinoma Type I and II.

Adenoid cystic carcinoma (ACC) is an uncommon salivary gland cancer with no approved therapies available to treat advanced, incurable disease. Recent molecular profiling efforts have identified two important subtypes: the more aggressive ACC-I is characterized by Notch pathway alterations and MYC amplification whereas ACC-II demonstrates a more indolent phenotype and TP63 overexpression. This retrospective observational cohort study involved de-identified samples from 438 patients with ACC with tumor samples sent for commercially-available molecular profiling (Caris Life Sciences). Next-generation whole-exome and whole-transcriptomic sequencing was performed on primary and metastatic samples. Immunostaining for PD-L1 and RNA deconvolution (quanTIseq) was used to explore the tumor immune microenvironment (TME). Real-world clinical and survival outcome metrics were extracted from insurance claims data. MYC expression was 1.61-fold higher (39.8 vs. 24.7; P < 0.0001) among NOTCH1-mutant ACC-I tumors, whereas MYB/L1 fusion rates were similar among ACC-I/II. The median B-cell fraction in the TME was higher among ACC-II (7.1% vs. 5.8%; P < 0.01), although infiltrating T cells subsets were low among either ACC subgroup (both <1%). When pooling systemic treatment categories, ACC-I patients had worse outcomes with available therapies (HR, 3.06; 95% confidence interval, 1.65-5.68; P < 0.01), with no significant difference in overall survival between ACC-I/II based on chemotherapy or VEGFR tyrosine kinase inhibitor exposure in smaller subsets. We confirmed the previously reported associations with MYC and TP63 in the prognostically relevant subgroups of ACC-I and -II, respectively, and report immunologic differences among these subtypes. Survival outcomes are comparatively worse in ACC-I regardless of treatment type.

Introduction to: The Rise of the Well-Being, Happiness, and Fulfillment Driven Organization

In this editorial we introduce the second of three special issues in the "Science of Life and Well-Being: A New Language for Humanity" series. The articles in this special issue provide insightful and helpful perspectives on the overall path toward a more flourishing future through organizations that prioritize well-being, happiness, and fulfillment.

Understanding the Unique Factors Affecting South Asian International (SAI) Student Transitions into PhD Programs in the US: An Interpretative Phenomenological Analysis

International students comprise over 50% of the graduate student population in the life sciences in the US, over 70% of whom are Asian. Research that aims to understand international students’ experiences has often treated Asian students as a monolith, discounting significant cultural and historical differences between regions in Asia that may affect students’ motivations for pursuing graduate degrees, their experiences in graduate school, and their identities as scientists in training. To begin to understand the experiences of SAI students as they transition to PhD programs in the sciences, we conducted an exploratory study in which we interviewed 10 SAI students and 12 US native students during the first six months of their doctoral programs. We performed a content analysis of the interview data with the aim of identifying factors that shaped students’ doctoral transitions. We then selected factors that were distinctive to SAI students. Finally, we carried out an interpretative phenomenological analysis to understand and describe the following factors that SAI students experienced as influencing their doctoral transitions: prior exposure to research; opportunities for networking; challenges with and affordances for acculturation; attitudes toward and understanding of mental health issues; financial affordances and constraints of pursuing a PhD, and barriers to communication. The results of this work have the potential to be useful to graduate programs seeking to ease SAI students’ transition to doctoral programs.

GO@ZnO/CuO sensitive selective detection of lactic acid photoelectrochemistry design of sensors

The photoelectrochemical lactate sensor is a sensor that integrates electrochemical and optoelectronic principles for detecting lactate concentration. Lactic acid is a metabolic product in organisms, and its concentration changes are closely related to the health status of the body. The development of highly sensitive and selective lactic acid sensors is of great significance for fields such as medical diagnosis, exercise physiology, and the food industry. This study used a simple hydrothermal method to prepare CuO and ZnO composite electrodes, with GO flake structures covering the surface of nanoparticles, providing a larger surface area and enhanced interaction. Advanced nanomaterials were used as the working electrode materials for electrochemical sensors, which generate current signals through specific reactions with lactic acid to enhance the sensitivity and stability of the sensors. The limit of detection (LOD) is 0.203 mM. Efficient monitoring of lactate concentration provides a novel and multifunctional method, which is of great significance for promoting research in the fields of medicine and life sciences.

Nanoprinted microstructure-assisted light incoupling into high-numerical aperture multimode fibers.

The coupling of light into optical fibers is limited by the numerical aperture (NA). Here, we show that large-area polymer axial-symmetric microstructures printed on silica multimode fibers improve their incoupling performance by two to three orders of magnitude beyond the numerical aperture limit. A ray-optical mathematical model describing the impact of the grating-assisted light coupling complements the experimental investigation. This study clearly demonstrates the improvement of incoupling performance by nanoprinting microstructures on fibers, opening new horizons, to the best of our knowledge, for multimode fiber applications in life sciences, quantum technologies, and "lab-on-fiber" devices.

Front Cover

Journal of Biophotonics focuses on the interdisciplinary field of biophotonics, publishing cutting‐edge research on interactions between light and biological material. The journal covers photonics research in areas such as life sciences, medicine and biomedicine, environmental science, nutrition, biology, physics, and chemistry, ranging from fundamental research to the latest clinical applications.image

Cover Picture: Engineering in Life Sciences 4'24

Cover Picture: Engineering in Life Sciences 4'24

Ethical Considerations in Animal Research: A Focus on China

Laboratory animals have played an indispensable role in advancing human knowledge and serve as essential subjects for generating reliable and precise outcomes in life science research. However, the ethical quandaries associated with animal testing, including issues related to their treatment and welfare, demand careful consideration. Similar to numerous developed nations, China has dedicated significant efforts to the protection of laboratory animals. This paper primarily examines China's legislative modifications concerning animal experiments within the cosmetic industry, conducts an ethical analysis of these legislative changes, and applies the 3Rs (Replace, Reduce, Refine) framework to propose practical recommendations for improving China's approach in this context.

Self-calibrating colorimetric sensor assisted deep learning strategy for efficient and precise Fe(II) detection

Colorimetry is to determine the component content usually comparing or measuring the “RGB” values, which has the advantages of quick response and visual comparison. However, the widely reported “RGB” models are limited by requiring professional operation such as accurate sampling, insufficient information, cumbersome calculation, etc., which has significantly restricted the promotional application. Inspired by the PAA-PVA gel with excellent absorbing-swelling ability and superior response ability, indefinite volume addition has a significant difference in the swelling phenomenon of PAA-PVA hydrogel, the extent of this enlargement is directly related to the amount of testing sample absorbed, which can be applied to reflect testing sample volume added. In addition, benefited from a comprehensive and precise parameter network, the deep learning (DL) algorithm solves the defects such as ambient light effect and manual extract RGB values of the colorimetric sensing analysis strategy. Considering Fe(II) is an essential element for life science and environmental monitoring, we have developed a self-calibrating colorimetric sensor assisted deep learning strategy for the Fe(II) detection with indefinite volume. It can directly complete the analysis of Fe(II) concentration via captured images, without going through a complicated process similar to the RGB model. Specifically, we integrated the advantages of colorimetric sensing strategy, absorbing-swelling hydrogel, and DL algorithm to develop a phenanthroline (PHEN) indicator doped PAA-PVA colorimetric sensor (PHEN-PAA-PVA). Then, 2,500 colorimetric response images were collected as the training dataset and assessed 2 DL algorithms and 7 machine learning (ML) algorithms. The convolutional neural network (CNN) model exhibited the most efficiency to quantify 1×10−5-9×10−4 mol/L Fe(II), with a limit of detection (LOD) of 3.3×10−6 mol/L. It provides a promising and blooming in-situ detection approach for Fe(II) analysis, contributes to health management and environment monitoring.