High Cost Research Articles

Neurocircle microsurgery model: Description of simulation-based training and exoscope

Background: Microsurgery is crucial in neurosurgery, requiring precise skills for interventions on delicate structures. Effective training is essential for developing these skills. In Peru and Latin America, however, there is a notable shortage of specialized training centers and high costs associated with foreign simulators, hindering the development of neurosurgical skills. To address this issue, the NeuroZone3D Research Center has initiated a project to create a national dataset on Peruvian craniometry and develop locally adapted training models. Methods: The “NeuroCircle Microsurgery Model” was created through a multi-phase process. Phase 1 involved designing a simulation platform using a 3D printer. Phase 2 focused on creating a realistic biomodel with polyurethane and advanced modeling techniques. In Phase 3, the biomodel was assembled and integrated into the platform. Phase 4 included using a wooden module, a cost-effective exoscope simulation, and surgical instruments to provide a realistic training environment. Results: The “NeuroCircle Microsurgery Model” provided a stable and effective training environment. Feedback from training sessions with neurosurgery residents and medical students indicated significant improvements in microsurgical skills. Participants valued the model’s realism and its role in enhancing their surgical proficiency. Conclusion: The “NeuroCircle Microsurgery Model” is a significant advancement in microsurgery training for Peru and Latin America. Its development provides a valuable, locally adaptable tool for improving surgical skills and addresses the critical training gaps in the region.

Nano ZnO modified amorphous carbon materials enabling long-cycle performance and high-capacity for lithium/sodium-ion batteries

Zinc-modified carbon materials exhibit high specific capacity, good safety, and low self-discharge, making them promising anode materials for lithium-ion and sodium-ion batteries. However, the high cost of raw materials, complex fabrication processes, and limited cycle life remain significant challenges to their further development. In this study, we elucidated a two-step process, simple in technique and suitable for large-scale production, for in-situ zinc modification of amorphous carbon. The synthesized carbon material exhibited superior reversible specific capacity (maintaining 780.27 mAh·g−1 capacity after 80 cycles at 50 mA·g−1 current density), high cycling stability (maintaining Coulombic efficiency at 100.06 % after 15,000 cycles at 10 A·g−1 current density), and good application value (achieving a capacity of 89.67 mAh·g−1 after 1000 cycles at 200 mA·g−1 current density for the assembled MFAC-Zn||NVP full cell). Through our investigation, we discovered that Nano ZnO not only directly participate in electrochemical reactions but also effectively improve the microstructure of amorphous carbon, increasing its three-dimensional axial stacking and providing more reaction sites. The enrichment of open pores significantly enhances the diffusion efficiency of ions within the carbon-based matrix. The superior diffusion ability of alkali metal-ion in the electrode is the reason for the excellent electrochemical performance of Zinc-modified amorphous carbon. Moreover, the incorporation of Nano ZnO aids in the formation of a robust and thin SEI layer. This significantly enhances the stability of the electrode materials during cycling and improves charge transfer efficiency during the charge-discharge process. Our study provides a new strategy for adjusting the internal microstructure of amorphous carbon materials to achieve high-performance lithium/sodium storage.

Enhancing intrinsic TGF-β signaling via heparan sulfate glycosaminoglycan regulation to promote mesenchymal stem cell capabilities and chondrogenesis for cartilage repair

Osteoarthritis burdens patients due to the limited regenerative capacity of chondrocytes. Traditional cartilage repair often falls short, necessitating innovative approaches. Mesenchymal stem cells (MSCs) show promise for regeneration. Heparan sulfate glycosaminoglycans (HS-GAGs) regulate cellular functions, making them a target for cartilage repair. This study highlights how Heparinase III (HepIII) cleaves intact HS-GAGs in bone marrow-derived MSCs (BM-MSCs), enhancing their capabilities and specifically promoting chondrogenesis. HepIII-treated BM-MSCs cultured in a hanging drop device for three days, significantly increased cell number and aggregation into a cell sphere with early chondrogenesis. HepIII promoted BM-MSCs toward chondrogenesis, increasing type II collagen, intact HS-GAGs, and sulfated GAG content, while upregulating chondrogenic and heparan sulfate proteoglycan genes. Treatment with the TGF-β inhibitor (SB-431542) in HepIII-treated BM-MSCs demonstrated enhanced intrinsic transforming growth factor-β (TGF-β) signaling and fibronectin expression. This approach also boosted BM-MSC self-renewal, immunosuppressive potential, and modified acetylated histone signatures, offering a cost-effective strategy for cartilage repair by addressing inflammation, metabolic changes, and the high costs of traditional TGF-β methods. From the results, HepIII-treated BM-MSCs show potential for use in combination with other biopolymers as injectable gels to improve cartilage repair in osteoarthritis patients in the near future.

Alkali activation of blast furnace slag using Bayer red mud as an alternative activator to prepare cemented paste backfill

Alkali-activated materials (AAMs) are increasingly used in mine backfilling as substitutes for Ordinary Portland Cement (OPC) due to their superior performance and environmental advantages. However, the high cost of commercially available alkaline activators such as sodium hydroxide and sodium silicate limit the widespread use of AAMs. This study investigates the use of Bayer red mud (RM) as an alternative activator to sodium hydroxide (SH), with blast furnace slag (BFS) as the precursor. Cemented paste backfill (CPB) was prepared by replacing 75% of OPC with these AAMs, combined with tailings and water. The results showed that CPB made with AAMs exhibited significantly better mechanical properties, with a 57% to 94% increase in uniaxial compressive strength (UCS) at 28 days compared to CPB made with OPC. Even with an RM-to-SH ratio of 2:1, UCS improved by 3% over SH-only samples. Analysis of hydration products and microstructure revealed that RM provides more alkaline ions, enhancing the formation of calcium-alumino-silicate-hydrate (C-A-S-H) and effectively filling capillary pores. This study demonstrates a cost-effective, environmentally friendly approach for producing AAMs, highlighting RM's potential as a sustainable alternative to commercial activators for CPB applications.

Hydrothermal synthesis of CeSe anchored on graphitic carbon nitride nanoclusters as an electrocatalyst for enhanced oxygen evolution reaction

The oxygen evolution reaction (OER) is a critical half-reaction in the process of water splitting, yet its practical application is hindered by slow kinetics and the high cost of conventional electrocatalysts. This study explores the potential of a novel CeSe/g-C3N4 nanocomposite as an efficient catalyst for OER in an alkaline environment. The CeSe/g-C3N4 nanocomposite exhibits exceptional electrocatalytic performance, demonstrated by a low overpotential of 196 mV at a current density of 10 mA cm−2 and a reduced onset potential of 1.29 V versus the reversible hydrogen electrode (RHE). Additionally, nanocomposite's Tafel slope of 58.14 mV/dec is significantly lower compared to pure CeSe (76.89 mV/dec) and multi-layered g-C3N4 (89.76 mV/dec), indicating superior kinetic behavior. Remarkably, the CeSe/g-C3N4 composite also demonstrates excellent electrochemical stability, maintaining its performance over a 40-hour period. These findings suggest that the CeSe/g-C3N4 nanocomposite not only enhances the electrocatalytic properties necessary for OER but also holds the potential to outperform traditional noble metal-based catalysts, paving the way for more cost-effective and efficient water-splitting technologies.

Boron doped Fe3S4/Co3S4 decorated with multi-wall carbon nanotubes as an efficient electrocatalyst for oxygen evolution reaction in alkaline media

Designing an exceedingly active and universal electrocatalyst for oxygen evolution reaction is utterly essential for commercial development. Herein, boron-doped cobalt-iron bimetallic sulfide synthesized through an easy single-step hydrothermal route is reported as electrocatalyst. The B-Fe3S4/Co3S4 was further decorated with the multi-wall carbon nano-tubes (MWCNT). The morphology of B-Fe3S4/Co3S4 was sphere like and it was converted to marigold flower-like after decorating it with MWCNT. The catalytic efficiency of Fe3S4/Co3S4 increased by doping with Boron and further increased by decorating with MWCNT. The prepared catalyst B-Fe3S4/Co3S4/MWCNT was tested for oxygen evolution reaction (OER) in alkaline solution and offered 319mV as overpotential and 60mV/decas Tafel slope value. In addition to it, the catalyst shows good stability, large Brunauer - Emmett-Teller (BET) surface area (30.24 m2/g), and low value of charge transfer resistance (6.5Ω), indicating that it is efficient catalyst and its high efficiency is associated with its morphology and ternary composite structure. The investigated low cost catalyst is as good as the commercially used high cost noble metal RuO2 catalysts.

Multi-headed Ensemble Residual CNN: A Powerful Tool for Fibroblast Growth Factor Prediction

Fibroblast Growth Factor (FGF) performs a significant role in the repair, nervous system, development, and maintenance, making it a promising target for treating neurological diseases such as Parkinson's, stroke, Alzheimer's, and Huntington's disorders. However, the detection of FGF remains challenging due to the high cost and time required by traditional methods. To address this issue, we propose the first sequence-based computational method, FGF-MERCNN, for identifying FGFs using deep learning. Novel datasets were constructed by converting primary sequences into numerical representations through Enhanced Group Amino Acid Composition (EGAAC), Dipeptide Deviation from Expected Mean (DDE), and Group Tripeptide Composition (GTPC). These features were combined and analyzed using several deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), Gated Recurrent Units (GRU), and Multiheaded Ensemble Residual CNN (MERCNN). Among these, the MERCNN model achieved superior performance with accuracies of 88.60% on the training set and 83.68% on the test set, as validated by 5-fold cross-validation. The results of this study represent a significant advancement in FGF detection, offering a faster, cost-effective solution with the potential to improve diagnostic and therapeutic strategies for neurological diseases.

A parameterized modeling method for analyzing welding residual stress in orthotropic steel deck

This paper presents a new parameterized method for establishing OSD models and analyzing WRS, and two plugins are developed based on the graphical user interface (GUI) of Abaqus. This parameterized method addresses the extremely high cost of manual modeling of OSD in Abaqus. In the two presented plugins, the OSD dimensions and welds size, the material properties, the welding heating source, and the mesh properties are parameterized, and the OSD model can be created automatically by the plugins without users’ pre-processing operations in Abaqus, which significantly facilitates the OSD modeling and the WRS analysis. The plugins developed in this study can provide the WRS analysis in the OSD with any dimensions and welding technologies, and the applicability of the plugins is verified by the consistency between the simulated WRS analysis results by plugins and those reported in the literature.

ENHANCING EFFICIENCY AND SUSTAINABILITY: GREEN ENERGY SOLUTIONS FOR WATER SUPPLY COMPANIES

Water supply enterprises significantly impact local communities by providing essential services. These companies face high electricity costs for water extraction, purification, and transportation, affecting efficiency and reliability. Therefore, implementing innovative technologies to enhance sustainability in the water supply sector is crucial. This article explores the opportunities and strategies for renewable energy transition at water supply companies. Using investment, sensitivity, and strategic analyses, as well as a case study approach, the research examines technologies, conceptual frameworks, mechanisms, and approaches to integrate green power into water supply operations, addressing high energy costs and promoting sustainability. The article identifies solar, wind, hydroelectric, biomass, and geothermal energy technologies as the most prominent renewable power solutions for water supply enterprises. The developed conceptual framework for implementing these technologies includes needs assessment and goal setting; resource assessment and technology selection; system design and integration; financing and investment; regulatory compliance and permitting; stakeholder engagement and capacity building; and monitoring, evaluation, and continuous improvement. The mechanisms and approaches to integrate green energy solutions within the developed framework can involve on-site renewable energy generation, power purchase agreements, energy storage and microgrid systems, energy efficiency and demand management, and collaborative and community-based models. As a case study, the article examines a 120-kW solar power plant project for a water supply enterprise, demonstrating profitability with a net present value of 60,370 USD and an internal rate of return exceeding 21%. The project's payback period is estimated at 8.38 years, acceptable within industry standards. Sensitivity analysis indicates the project's financial resilience. Increasing electricity prices will boost profitability, justifying the solar power plant investment amid inflation and economic instability. Additionally, the project ensures reliable water transport and environmental benefits by reducing CO2 emissions through solar energy use. Thus, transitioning to renewable energy at water supply enterprises is feasible and essential for long-term sustainability, transforming operations to be more resilient, efficient, and environmentally friendly.

Quality improvement using lean six sigma in air conditioning products

Air Conditioners (AC) are essential electronic products for residential and industrial buildings in tropical countries or during warm weather in general. The penetration ratio of electronic Air Conditioners (AC) has been increasing every year. Data collection and processing were conducted directly by the author through observation, measurement, and calculation on the heat exchanger production of Air Conditioners (AC). In the production cost of Air Conditioners (AC), the heat exchanger accounts for the second highest cost after the compressor making up 19% of the total cost. The high cost is caused by pipe leaks, with a percentage of 73.7%. This research aims to analyze the improvement of Air Conditioner (AC) production quality using the Six Sigma method. The defect rate in the heat exchanger, evaluated through the Define, Measure, Analyze, Improve, Control (DMAIC) method originating from Six Sigma, was found to be 1,144.8 defects per million opportunities. Further analysis was conducted with the Failure Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) methods. This research was conducted at XYZ company by conducting interviews and observations for 3 months. FMEA and FTA analysis resulted in the top two failure risks based on the Risk Priority Number (RPN): flaring diameter too big and unstable brazing skill. In the manual brazing process, it was found that work by operators with level A certification produced 9% below standard outcomes. An improvement was obtained by changing the punch flaring size to 8.5mm and conducting skill certification and training for brazing operators. The results of the proposed improvement implementation led to a reduction in the DPMO value to 403.35, equivalent to a decrease of 64.7%. The defect ratio in production decreased by 38.1%, exceeding the previous target of 26.2%, equivalent to $31.96/month/unit. The results of this study are horizontal, allowing the implementation of improvements made in the heat exchanger production process to be expanded to other cooling electronic production processes.

Enhancing IVF Outcomes with Artificial Intelligence: Current Advances and Future Possibilities

The integration of Artificial Intelligence (AI) into In Vitro Fertilization (IVF) practices has marked a revolutionary shift in reproductive medicine, offering enhanced precision, efficiency, and personalized treatment plans. The rapid advancement of Artificial Intelligence (AI) has led to significant innovations in the field of reproductive medicine, particularly in In Vitro Fertilization (IVF). Traditional IVF procedures, while effective, often face challenges such as variable success rates, high costs, and the emotional burden on patients due to multiple treatment cycles. AI offers a promising solution to these issues by enhancing accuracy, personalization, and efficiency throughout the IVF process. AI algorithms have shown remarkable capabilities in diagnosing infertility by analyzing complex datasets from hormone profiles, genetic testing, and medical imaging, enabling early identification of conditions like polycystic ovary syndrome (PCOS) and endometriosis. Moreover, one of the most promising applications of AI in IVF is embryo grading. However, AI systems have been developed to objectively evaluate embryos based on time-lapse imaging, morphology, and other parameters, improving the selection process. Additionally, AI has been instrumental in optimizing ovarian stimulation protocols by analyzing patient data to determine the appropriate medication dosage, minimizing the risk of ovarian hyperstimulation syndrome (OHSS). This review discusses the current state of AI integration in fertility treatments, successful case studies, and ongoing research to develop more sophisticated AI models. Overall, AI holds immense promise in making IVF more accessible, affordable, and successful for patients worldwide, ushering in a new era of precision medicine in reproductive health.

Study on the Effect of Natural dyes and Synthetic dyes on Textile Fabric

Natural dyes are very useful for dyeing textile fibre. Natural dyes have been used for long back. In the Mughal regime, it was found that natural dyes were used to dye textile materials. Starting from the Muslin to the household textiles the use of natural dyes was significant. For more than a hundred years, it was evident that synthetic dyes become popular for dyeing textile material. Natural dyes for jamdani sarees are not easily available in Bangladesh. These dyes are imported from outside of Bangladesh and for this reason, this type of dyes is not available in the local market. But at the same time, the risk of using synthetic dyes is high for human health because of cause different health hazards like breathing problems, skin diseases and skin cancer. Compared to synthetic dyes natural dyes are not easily available and they are costly at the same time. The unavailability of natural dyes made the use of synthetic dyes more popular among the manufacturers of textile products. Though natural dyes are sustainable, due to unavailability and high cost it is hindering the acceptance to use natural dyes. In this study, it was evident that natural dyes normally have significant eco-friendly properties especially the absence of banned amines compared to synthetic dyes. It was found that synthetic dyes have banned amines.

The Impact of the Formation of Petroleum Fund Policy on the Sustainability of Oil and Gas Activities in Indonesia

Business activities in the oil and gas sector certainly require high costs from upstream to downstream. From upstream to downstream of Indonesian oil and gas, the gap between production and consumption is getting wider, namely 616 thousand BOPD production and 1,471 MBOPD consumption and refinery capacity is still below the consumption figure, namely 1,151 million barrels per day. The need for high costs can be minimized by establishing a policy called the Petroleum Fund. This Petroleum Fund is taken from state revenues from the oil and gas sector for specific purposes to ensure the sustainability of oil and gas activities for future generations. Petroleum funds, especially in Indonesia, can be used for several important things such as investment capital on the upstream side to increase potential reserve status. On the downstream side, it can be used to increase refinery capacity so that fuel oil imports decrease. The fluctuating nature of oil prices is certainly a threat that must be overcome, this is anticipated by forming this policy so that fuel prices remain affordable for the public. Then efforts to reduce greenhouse gas emissions become an important thing that must be achieved by oil and gas companies as well as increasing human resource capacity in areas around the oil and gas industry.

The Flexural Behavior of Reinforced Ultra-High Performance Engineering Cementitious Composite (UHP-ECC) Beams Fabricated with Polyethylene Fiber (Numerical and Analytical Study)

Ultra-high performance engineered cementitious composite (UHP-ECC), which is a new and ductile version of concrete, has attracted researchers recently due to its exceptional mechanical properties: its very high compressive strength (from 100 to 200 MPa) and very high tensile strain capacity (not less than 3% and up to 8%). However, the available experimental literature is small due to its very high cost. To overcome the high cost of the experiments of UHP-ECC, the finite element modeling package ANSYS was used to create a new modeling technique using the Menetrey–Willam constitutive model, recently added to ANSYS. This technique was validated using previous experimental results for UHP-ECC beams and found to be accurate and effective. The previous FE model was used to conduct a parametric study and the variables—the compressive strength of the concrete, the percentage of the volume content of polyethylene fibers, the tensile reinforcement ratio, and the span-to-depth ratio—were found to be effective upon the flexure behavior of the reinforced UHP-ECC beams. As the analysis and design of UHP-ECC beams fabricated with polyethylene fiber are not available yet through design codes, an analytical model including some equations was deduced to calculate the flexure capacity of such beams. The results of the parametric study were used to investigate the validity and accuracy of the analytical model. The proposed equations demonstrated a good estimation compared with the numerical analysis results.

Point-of-care test of blood Plasmodium RNA within a Pasteur pipette using a novel isothermal amplification without nucleic acid purification

BackgroundResource-limited regions face a greater burden of infectious diseases due to limited access to molecular tests, complicating timely diagnosis and management. Current molecular point-of-care tests (POCTs) either come with high costs or lack adequate sensitivity and specificity. To facilitate better prevention and control of infectious diseases in underserved areas, we seek to address the need for molecular POCTs that better align with the World Health Organization (WHO)’s ASSURED criteria—Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable to end users.MethodsA novel molecular POCT, Pasteur Pipette-assisted isothermal probe amplification (pp-IPA), was developed for malaria detection. Without any microfluidics, this method captures Plasmodium 18S rRNA in a modified Pasteur pipette using tailed genus-specific probes. After washing, the bound tailed probes are ligated to form a template for subsequent novel isothermal probe amplification using a pair of generic primers, bypassing nucleic acid extraction and reverse transcription. The method was assessed using cultured Plasmodium and compared with real-time quantitative reverse transcription PCR (RT-qPCR) or reverse transcription loop-mediated isothermal amplification (RT-LAMP) in clinical blood samples.ResultsThe entire assay is completed in 60–80 min with minimal hands-on time, using only a Pasteur pipette and a water bath. The pp-IPA’s analytical sensitivity is 1.28 × 10–4 parasites/μl, with 100% specificity against various blood-borne pathogens causing malaria-like symptoms. Additionally, pp-IPA needs only liquid-transfer skill for operation and the cost is around USD 0.25 per test, making it at least 300 times lower than mainstream POCT platforms.ConclusionsDesigned to improve the accessibility of molecular detection in resource-limited settings, pp-IPA’s simplicity, affordability, high sensitivity/specificity, and minimal equipment requirements make it a promising point-of-care pathogen identification tool in resource-constrained regions.Graphical

Impact of Building Information Modelling in achieving Sustainable Efficiency

This review paper explores into the impact of Building Information Modelling (BIM) implementation on sustainable efficiency within construction projects. It systematically examines BIM’s influence on sustainable project performance, its utilization in construction organizations, and the identification of barriers and enablers affecting the integration of sustainable practices through BIM, with recommendations to overcome these barriers. Methodologically, the review synthesizes extensive literature on BIM in construction field is conducted through systematic searches across databases like Scopus and Google Scholar from 2006 to 2024. The findings reveal BIM’s transformative role in the construction industry’s digital evolution, enhancing collaboration, design clash detection, cost estimation, and scheduling, with promising potential for environmentally conscious design through its integration with sustainable practices. Nonetheless, challenges such as high adoption costs, expertise limitations, and software compatibility hinder widespread BIM implementation, particularly in sustainability-focused projects. The study underscores the immense potential of BIM in supporting sustainability within construction, emphasizing the necessity for guidance, training, modified business models, and collaborative efforts to unlock its full potential. This review bridges the gap between BIM’s potential and practical implementation, providing insights into leveraging BIM for sustainable construction practices, thereby promising a more environmentally conscious future for the construction industry.

Robotic assistants for the rehabilitation of communication disorders in children: A proposal based on MTO manufacturing for developing countries

Language is fundamental to human communication, allowing individuals to express and exchange ideas, thoughts, and emotions. In early childhood, some children experience communication disorders that impede their ability to articulate words correctly, posing significant challenges to their learning and development. This issue is exacerbated in developing countries, where limited resources and a lack of technological tools hinder access to effective speech therapy. Traditional speech therapy remains vital, but the latest technological advancements have introduced robotic assistants to enhance therapy for communication disorders. Despite their potential, these technologies are often inaccessible in developing regions due to high production costs and a lack of sustainable manufacturing models. For these reasons, this paper presents “FONA,” a robotic assistant that employs rule-based expert systems to provide tactile, auditory, and visual stimuli. FONA supports children aged 3 to 6 in speech therapy by delivering exercises such as syllable production, word formation, and pictographic storytelling of various phonemes. Notably, FONA was successfully tested on children with cochlear implants, reducing the number of sessions required to produce isolated phonemes. The paper also introduces an innovative analysis of the Make To Order (MTO) manufacturing system for producing FONA in developing countries. This analysis explores two key perspectives: collaborative networks and entrepreneurship, offering a sustainable production model. In a pilot experiment, FONA significantly improved children’s attention spans, increasing the period by 17 min. Furthermore, the economic analysis demonstrates that producing FONA through collaborative networks can significantly reduce costs, making it more accessible to institutions in developing countries. The findings suggest that the project is viable for a five-year period, providing a sustainable and effective solution for addressing communication disorders in children.

A Comparative Study of AStar, LPA* and DStarLite Path Planning Algorithms Based on Matlab

In the realm of robotics and autonomous systems, path planning is a pivotal component that determines the efficacy and safety of navigational tasks. With the proliferation of autonomous vehicles, drones, and mobile robots, the need for efficient and adaptive path planning algorithms has become increasingly acute. This paper studies AStar, LPA and DStarLite path planning algorithms based on Matlab platform, and compares their performance through simulation experiments. AStar algorithm is simple and widely applicable, but it has some shortcomings in path smoothness and computational efficiency. LPA improves path smoothness by introducing dynamic cost updating, but it may sacrifice some computational efficiency. The DStarLite algorithm performs well in dynamic environments with an efficient incremental update strategy that maintains high path smoothness and low computational costs. The experimental results show that DStarLite is the fastest in most cases, LPA* and DStarLite are superior to AStar in path smoothness. Future research may explore combining the advantages of each algorithm to develop more efficient, flexible and robust path planning algorithms to cope with complex and changeable actual scenarios.

Screening and Selection of a New Medium and Culture Conditions for Diosgenin Production via Microbial Biocatalysis of SYt1

Diosgenin (DSG) is a phytosterol saponin mainly found in Dioscorea zingiberensis C.H. Wright. It has shown promising results in treating various diseases such as cancer, diabetes, arthritis, asthma, and cardiovascular diseases. Diosgenin is also an important medicinal chemical for synthesizing various steroid medicines. The production of diosgenin by acid hydrolysis generates a large amount of wastewater, leading to severe environmental pollution. However, producing diosgenin through microbial fermentation can effectively reduce environmental pollution. Numerous studies have demonstrated that various microorganisms can produce diosgenin via solid-state fermentation. Nevertheless, due to the complexity, high maintenance costs, uneven heat production, and other characteristics of solid-state fermentation, it is not commonly used in the industrial production of diosgenin. In contrast, liquid fermentation offers advantages such as simple operation, easy maintenance, and stable fermentation, making it more suitable for the industrial production of diosgenin. However, few studies have focused on producing diosgenin using liquid fermentation. In this study, endophytic Bacillus licheniformis SYt1 was used to produce diosgenin via liquid fermentation, with Dioscorea tuber powder as a substrate. Soxhlet extraction and silica gel column chromatography were employed to identify the diosgenin from the liquid fermentation products. Suitable fermentation conditions were screened and identified. The environmental variables that significantly affect the diosgenin yield were determined by the Plackett–Burman design (P-BD) with eight factors. The three factors (peptone, yeast extract powder and inorganic salt) with the greatest influence on the diosgenin yield were selected and further optimized using a response surface methodology (RSM). The final culture conditions were determined to be 35.79 g/L of peptone, 14.56 g/L of yeast extract powder, and 1.44 g/L of inorganic salt. The yield of diosgenin under these conditions was 132.57 mg/L, which was 1.8 times greater than the yield under pre-optimization conditions. This effective, clean, and promising liquid fermentation method possesses the potential to replace the traditional acid hydrolysis method for the industrial production of diosgenin.

Automated-Screening Oriented Electric Sensing of Vitamin B1 Using a Machine Learning Aided Solid-State Nanopore.

Micronutrient detection and identification at the single-molecule level are paramount for both clinical and home diagnostics. Analytical tools such as high-performance liquid chromatography and liquid chromatography-tandem mass spectrometry have been widely used but include a high instrument cost and prolonged analysis time. Here, as a model system, by merging nanopore signatures with machine learning algorithms, we propose an automated electric sensing strategy to identify vitamin B1 and its phosphorylated derivatives with good accuracy. Further, the relationship between vitamin B1 dynamics and nanopore signatures is examined. To understand the machine-decision-making process, Shapley additive explanations are made. Using a machine learning aided solid-state nanopore, we pave the way for next-generation micronutrient detection.