The analysis of action potentials and other membrane voltage fluctuations provides a powerful approach for interrogating the function of excitable cells. However, a major bottleneck in the interpretation of this critical data is the lack of intuitive, agreed-upon software tools for its analysis. Here, we present SanPy, an open-source and freely available software package for the analysis and exploration of whole-cell current-clamp recordings written in Python. SanPy provides a robust computational engine with an application programming interface. Using this, we have developed a cross-platform desktop application with a graphical user interface that does not require programming. SanPy is designed to extract common parameters from action potentials, including threshold time and voltage, peak, half-width, and interval statistics. In addition, several cardiac parameters are measured, including the early diastolic duration and rate. SanPy is built to be fully extensible by providing a plugin architecture for the addition of new file loaders, analysis, and visualizations. A key feature of SanPy is its focus on quality control and data exploration. In the desktop interface, all plots of the data and analysis are linked, allowing simultaneous data visualization from different dimensions with the goal of obtaining ground-truth analysis. We provide documentation for all aspects of SanPy, including several use cases and examples. To test SanPy, we performed analysis on current-clamp recordings from heart and brain cells. Taken together, SanPy is a powerful tool for whole-cell current-clamp analysis and lays the foundation for future extension by the scientific community.

In recent years, there has been a surge of interest in tumor microenvironment-associated cancer vaccine therapies. These innovative treatments aim to activate and enhance the body\'s natural immune response against cancer cells by utilizing specific antigens present in the tumor microenvironment. The goal is to achieve a complete clinical response, where all measurable cancer cells are either eliminated or greatly reduced in size. With their potential to revolutionize cancer treatment, these therapies represent a promising avenue for researchers and clinicians alike. Despite over 100 years of research, the success of therapeutic cancer vaccines has been variable, particularly in advanced cancer patients, with various limitations, including the heterogeneity of the tumor microenvironment, the presence of immunosuppressive cells, and the potential for tumor escape mechanisms. Additionally, the effectiveness of these therapies may be limited by the variability of the patient\'s immune system response and the difficulty in identifying appropriate antigens for each patient. Despite these challenges, tumor microenvironment-targeted vaccine cancer therapies have shown promising results in preclinical and clinical studies and have the potential to become a valuable addition to current cancer treatment and \"curative\" options. While chemotherapeutic and monoclonal antibody treatments remain popular, ongoing research is needed to optimize the design and delivery of these therapies and to identify biomarkers that can predict response and guide patient selection. This comprehensive review explores the mechanisms of cancer vaccines, various delivery methods, and the role of adjuvants in improving treatment outcomes. It also discusses the historical background of cancer vaccine research and examines the current state of major cancer vaccination immunotherapies. Furthermore, the limitations and effectiveness of each vaccine type are analyzed, providing insights into the future of cancer vaccine development.

BACKGROUND: Using fungal biomass for biocatalysis is a potential solution for the expensive cost of the use o enzymes. Production of fungal biomass with effective activity requires optimizing the cultivation conditions.
RESULTS: Rhizopus stolonifer biomass was optimized for transesterification and hydrolysis of waste frying oil (WFO). Growth and biomass lipolytic activities of R. stolonifer improved under shaking conditions compared to static conditions, and 200 rpm was optimum. As biomass lipase and transesterification activities inducer, olive oil was superior to soybean, rapeseed, and waste frying oils. Biomass produced in culture media containing fishmeal as an N-source feedstock had higher lipolytic capabilities than corn-steep liquor and urea. Plackett Burman screening of 9 factors showed that pH (5-9), fishmeal (0.25-1.7%, w/v), and KH2PO4 (0.1-0.9%, w/v) were significant factors with the highest main effect estimates 11.46, 10.42, 14.90, respectively. These factors were selected for response surface methodology (RSM) optimization using central composite design (CCD). CCD models for growth, biomass lipase activity, and transesterification capability were significant. The optimum conditions for growth and lipid modification catalytic activities were pH 7.4, fishmeal (2.62%, w/v), and KH2PO4 (2.99%, w/v).
CONCLUSIONS: Optimized culture conditions improved the whole cell transesterification capability of Rhizopus stolonifer biomass in terms of fatty acid methyl ester (FAME) concentration by 67.65% to a final FAME concentration of 85.5%, w/w.

The analysis of action potentials and other membrane voltage fluctuations provide a powerful approach for interrogating the function of excitable cells. Yet, a major bottleneck in the interpretation of this critical data is the lack of intuitive, agreed upon software tools for its analysis. Here, we present SanPy, a Python-based open-source and freely available software pipeline for the analysis and exploration of whole-cell current-clamp recordings. SanPy provides a robust computational engine with an application programming interface. Using this, we have developed a cross-platform graphical user interface that does not require programming. SanPy is designed to extract common parameters from action potentials including threshold time and voltage, peak, half-width, and interval statistics. In addition, several cardiac parameters are measured including the early diastolic duration and rate. SanPy is built to be fully extensible by providing a plugin architecture for the addition of new file loaders, analysis, and visualizations. A key feature of SanPy is its focus on quality control and data exploration. In the desktop interface, all plots of the data and analysis are linked allowing simultaneous data visualization from different dimensions with the goal of obtaining ground truth analysis. We provide documentation for all aspects of SanPy including several use cases and examples. To test SanPy, we have performed analysis on current-clamp recordings from heart and brain cells. Taken together, SanPy is a powerful tool for whole-cell current-clamp analysis and lays the foundation for future extension by the scientific community.

While analytical measurements provide the quantitative estimation of the total amount of metals present in a sample, they do not reflect the truly bioavailable fraction of metal which reflects the adverse biological effect. Hence the development of monitoring tools for detecting bioavailable toxic metals has become a priority in environmental monitoring activities. An optical whole-cell biosensor was constructed using the microalga Scenedesmus subspicatus MM1 immobilizing in inorganic silica hydrogels using the sol-gel technique to detect bioavailable Cadmium (Cd2+), Copper (Cu2+) and Zinc (Zn+) in freshwater. Conditions for optimum biosensor performance have been established regarding effective pH range, cell density, exposure time, and storage stability. The optimum response for the biosensor was dependent on the pH of the matrix, cell concentration and exposure time were derived. The biosensor was operational for four weeks. The limit of detection for the algal biosensor was determined as 9.0 × 10-1, 9.1 × 10-1, and 8.8 × 10-1 mg/L for Cd, Cu and Zn, respectively. Whole-cell cell biosensor will be highly useful since it comprises a single microalgal species able to detect the bioavailable content of Cd2+, Cu2+, and Zn2+ in freshwater.

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Acinetobacter baumannii causes multi-system diseases in both nosocomial settings and a pre-disposed general population. The bacterium is not only desiccation-resistant but also notoriously resistant to multiple antibiotics and drugs of last resort including carbapenem, colistin, and sulbactam. The World Health Organization has categorized carbapenem-resistant A. baumannii at the top of its critical pathogen list in a bid to direct urgent countermeasure development. Several early-stage vaccines have shown a range of efficacies in healthy mice, but no vaccine candidates have advanced into clinical trials. Herein, we report our findings that both an ionizing γ-radiation-inactivated and a non-ionizing ultraviolet C-inactivated whole-cell vaccine candidate protects neutropenic mice from pulmonary challenge with virulent AB5075, a particularly pathogenic isolate. In addition, we demonstrate that a humoral response is sufficient for this protection via the passive immunization of neutropenic mice.

Hippocampal ripples are transient population bursts that structure cortico-hippocampal communication and play a central role in memory processing. However, the mechanisms controlling ripple initiation in behaving animals remain poorly understood. Here we combine multisite extracellular and whole-cell recordings in awake mice to contrast the brain state and ripple modulation of subthreshold dynamics across hippocampal subfields. We find that entorhinal input to the dentate gyrus (DG) exhibits UP and DOWN dynamics with ripples occurring exclusively in UP states. While elevated cortical input in UP states generates depolarization in DG and CA1, it produces persistent hyperpolarization in CA3 neurons. Furthermore, growing inhibition is evident in CA3 throughout the course of the ripple buildup, while DG and CA1 neurons exhibit depolarization transients 100 ms before and during ripples. These observations highlight the importance of CA3 inhibition for ripple generation, while pre-ripple responses indicate a long and orchestrated ripple initiation process in the awake state.

Pertussis is a common respiratory infection caused by the bacterium Bordetella pertussis. Although most cases occur in developing countries, it is considered endemic globally. The World Health Organization estimates there are 20-40 million cases of pertussis annually. Pertussis vaccines played a pivotal role in reducing the burden of pertussis disease as well as infant morbidity and mortality. Although the two forms of pertussis vaccine are effective, each has its advantages and drawbacks. This review aims to review the current knowledge on pertussis vaccines, emphasizing vaccine effectiveness in different populations within a community. Clinical trials have shown favorable vaccine efficacy with acellular pertussis (aP)vaccine. However, observational and population-level studies showed that introducing at least a single dose of whole-cell pertussis (wP) vaccine within the routine immunization schedule is associated with better disease protection and a longer duration of immunity. On the other hand, wP vaccine is more reactogenic and associated with higher adverse events. Therefore, the selection of vaccine should be weighed against the effectiveness, reactogenicity, and cost-effectiveness. Due to its safety profile, aP vaccine can be offered to wider population groups. Booster adolescent and pregnant immunization programs have been implemented globally to control outbreaks and protect vulnerable infants. Due to the variable effectiveness performance of both vaccines, different countries adopted distinctive immunization programs. Determining the right vaccination approach depends on financial consideration, immunization program infrastructure, adverse event monitoring, and pertussis surveillance in the community.

Identifying, isolating, and obtaining naturally occurring transcription factors (TFs) is crucial for developing transcription-dependent biosensors. However, identifying and optimizing TFs for given molecules requires extensive time and effort. Accordingly, here, we report a strategy for the de novo design of a nonnatural TF, DLA, on the basis of a subtle conformational change of the ligand-binding domain (LBD) after the binding of a target molecule with its receptor. For the de novo design of DLA, we applied molecular dynamics to simulate different conformational states of DLA in order to understand the complete activity of DLA, which involves shortening of the distance between the DNA-binding domain (DBD) and the activation domain (AD) after progesterone binds to its LBD within DLA. The simulated results suggested that prokaryotic LexA, a truncated LBD from the progesterone receptor, and prokaryotic B42 together constitute DLA with a TF function. As a proof of concept, DLA was used as a transcription activator controlling the transcription of green fluorescent protein to construct an S. cerevisiae biosensor for progesterone detection. The progesterone-specific biosensor was successfully constructed with a sensitivity index EC50 of 27 μg/L, working range (0.16-60 μg/L), and time-to-detection (2.5 h). Ultimately, a low-cost, user-friendly kit was developed for the rapid detection of progesterone in the clinic. Theoretically, this work can also be used to develop a variety of other biosensors by employing the same strategy.