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Introduction: Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences., Objective: Our objective is to compare the performance of the traditional logistic regression with other machine learning (ML) models in identifying parents who are more likely to have depression symptoms to prioritize screening of at-risk parents. We used data obtained from parents of infants discharged from the NICU at Children's National Hospital ( n  = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors., Study Design: Our study design optimized eight ML algorithms - Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network - to identify the main risk factors associated with parental depression. We compared models based on the area under the receiver operating characteristic curve (AUC), positive predicted value (PPV), sensitivity, and F-score., Results: The results showed that all eight models achieved an AUC above 0.8, suggesting that the logistic regression-based model's performance is comparable to other common ML models., Conclusion: Logistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sadjadpour, Hosseinichimeh, Abedi and Soghier.)

In this work, it is studied the photolysis, electrolysis, and photo-electrolysis of a mixture of pharmaceutics (sulfadiazine, naproxen, diclofenac, ketoprofen and ibuprofen) contained in two very different types of real water matrices (obtained from surface and porewater reservoirs), trying to clarify the role of the matrix on the degradation of the pollutants. To do this, a new metrological approach was also developed for screening of pharmaceuticals in waters by capillary liquid chromatography mass spectrometry (CLC-MS). This allows the detection at concentrations lower than 10 ng mL -1 . Results obtained in the degradation tests demonstrate that inorganic composition of the water matrix directly influences on the efficiency of the drugs removal by the different EAOPs and better degradation results were obtained for experiments carried out with surface water. The most recalcitrant drug studied was ibuprofen for all processes evaluated, while diclofenac and ketoprofen were found to be the easiest drugs for being degraded. Photo-electrolysis was found to be more efficient than photolysis and electrolysis, and the increase in the current density was found to attain a slight improvement in the removal although with an associated huge increase in the energy consumption. The main reaction pathways for each drug and technology were also proposed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Cervical myelopathy (CM) causes several symptoms such as clumsiness of the hands and often requires surgery. Screening and early diagnosis of CM are important because some patients are unaware of their early symptoms and consult a surgeon only after their condition has become severe. The 10-second hand grip and release test is commonly used to check for the presence of CM. The test is simple but would be more useful for screening if it could objectively evaluate the changes in movement specific to CM. A previous study analyzed finger movements in the 10-second hand grip and release test using the Leap Motion, a noncontact sensor, and a system was developed that can diagnose CM with high sensitivity and specificity using machine learning. However, the previous study had limitations in that the system recorded few parameters and did not differentiate CM from other hand disorders., Objective: This study aims to develop a system that can diagnose CM with higher sensitivity and specificity, and distinguish CM from carpal tunnel syndrome (CTS), a common hand disorder. We then validated the system with a modified Leap Motion that can record the joints of each finger., Methods: In total, 31, 27, and 29 participants were recruited into the CM, CTS, and control groups, respectively. We developed a system using Leap Motion that recorded 229 parameters of finger movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used for machine learning to develop the binary classification model and calculated the sensitivity, specificity, and area under the curve (AUC). We developed two models, one to diagnose CM among the CM and control groups (CM/control model), and the other to diagnose CM among the CM and non-CM groups (CM/non-CM model)., Results: The CM/control model indexes were as follows: sensitivity 74.2%, specificity 89.7%, and AUC 0.82. The CM/non-CM model indexes were as follows: sensitivity 71%, specificity 72.87%, and AUC 0.74., Conclusions: We developed a screening system capable of diagnosing CM with higher sensitivity and specificity. This system can differentiate patients with CM from patients with CTS as well as healthy patients and has the potential to screen for CM in a variety of patients., (©Takafumi Koyama, Ryota Matsui, Akiko Yamamoto, Eriku Yamada, Mio Norose, Takuya Ibara, Hidetoshi Kaburagi, Akimoto Nimura, Yuta Sugiura, Hideo Saito, Atsushi Okawa, Koji Fujita. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org), 03.10.2022.)

Sugar transporter research focuses on the sugar uptake into cells. Under certain physiological conditions, however, the intracellular accumulation and secretion of carbohydrates (efflux) are relevant processes in many cell types. Currently, no cell-based system is available for specifically investigating glucose efflux. Therefore, we designed a system based on a hexose transporter-deficient Saccharomyces cerevisiae strain, in which the disaccharide maltose is provided as a donor of intracellular glucose. By deleting the hexokinase genes, we prevented the metabolization of glucose, and thereby achieved the accumulation of growth-inhibitory glucose levels inside the cells. When a permease mediating glucose efflux is expressed in this system, the inhibitory effect is relieved proportionally to the capacity of the introduced transporter. The assay is thereby suitable for screening of transporters and quantitative analyses of their glucose efflux capacities. Moreover, by simultaneous provision of intracellular glucose and extracellular xylose, we investigated how each sugar influences the transport of the other one from the opposite side of the membrane. Thereby, we could show that the xylose transporter variant Gal2N376F is insensitive not only to extracellular but also to intracellular glucose. Considering the importance of sugar transporters in biotechnology, the assay could facilitate new developments in a variety of applications., (© The Author(s) 2022. Published by Oxford University Press on behalf of FEMS.)

Chinese hamster ovary (CHO) cells are by far the most commonly used mammalian expression system for recombinant expression of therapeutic proteins in the pharmaceutical industry. The development of high-yield stable cell lines requires processes of transfection, selection, screening and adaptation, among which the screening process requires tremendous time and determines the level of forming highly productive monoclonal cell lines. Therefore, how to achieve productive cell lines is a major question prior to industrial manufacturing. Cell line development (CLD) is one of the most critical steps in the production of recombinant therapeutic proteins. Generation of high-yield cell clones is mainly based on the time-consuming, laborious process of selection and screening. With the increase in recombinant therapeutic proteins expressed by CHO cells, CLD has become a major bottleneck in obtaining cell lines for manufacturing. The basic principles for CLD include preliminary screening for high-yield cell pool, single-cell isolation and improvement of productivity, clonality and stability. With the development of modern analysis and testing technologies, various screening methods have been used for CLD to enhance the selection efficiency of high-yield clonal cells. This review provides a comprehensive overview on preliminary screening methods for high-yield cell pool based on drug selective pressure. Moreover, we focus on high throughput methods for isolating high-yield cell clones and increasing the productivity and stability, as well as new screening strategies used for the biopharmaceutical industry., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yang, Zhang, Xiao, Li and Wang.)

Screening high nitrogen (N) efficiency crops is crucial to utilize resources rationally and reduce N losses. In this research, the biomass, morphological and N-related parameters of 28 alfalfa ( Medicago sativa L.) cultivars were assessed at seedling stage. Then, we selected representative materials to compare the changes in stem-leaf dry weight (SDW), total root length (RL) and plant N accumulation (PNA) during whole period. Lastly, we analyzed the expressions of NRT2 and AMT1 genes of alfalfa cultivars. The correlation coefficients between SDW, PDW, RL, RV, SNA, RNA, and PNA were all in the range of 0.522∼0.996. The coefficient of variations of SDW, PDW, RL, RV, SNA and PNA were all more than 20% under low and medium N levels. Though the comprehensive evaluation and cluster analysis, the comprehensive value of LW6010, Gannong NO.5, Longmu 806, Giant 2, Giant 601, Zhaodong, Crown were greater than 0.5 under low and medium N levels; the comprehensive value of Gannong NO.3, Gannong NO.4, Xinjiangdaye, Xinmu NO.1 were less than 0.5 under low N level, but were greater than 0.5 under medium N level. The comprehensive value of Gannong NO.7 Gannong NO.9, Longmu 801, Gongnong NO.3, Elite, Sadie 10, Giant 551 were greater than 0.5 under low N level, but were lesser than 0.5 under medium N level; and those of Longdong, Gannong NO.8, Gongnong NO.1, Reindee, Goldqueen, Weston, Tourists, Giant 6, Algonquin, Sadie 7 were lesser than 0.5 under low and medium N levels. Four N efficiency types of alfalfa cultivars were classified: (1) Very efficient; (2) Efficient; (3) Anti-efficient; and (4) Inefficient.The SDW, RL and PNA of LW6010 were higher than Longdong in each growth period. The expressions of NRT2 and AMT1 genes were highest for LW6010, and lowest for Longdong. So, N efficiency parameters assessed at seedling stage include: SDW, PDW, RL, RV, SNA and PNA. We developed new classification system of N efficiency types of alfalfa cultivars. It proved its effectiveness on 28 alfalfa in China., Competing Interests: The authors declare there are no competing interests., (©2022 Liu et al.)

Optimizing appropriate signal peptides in mammalian cell-based protein production is crucial given that most recombinant proteins produced in mammalian cells are thought to be secreted proteins. Until now, most studies on signal peptide in mammalian cells have replaced native signal peptides with well-known heterologous signal peptides and bioinformatics-based signal peptides. In the present study, we successfully established an in vitro screening system for synthetic signal peptide in CHO cells by combining a degenerate codon-based oligonucleotides library, a site-specific integration system, and a FACS-based antibody detection assay. Three new signal peptides were screened using this new screening system, confirming to have structural properties as signal peptides by the SignalP web server, a neural network-based algorithm that quantifies the signal peptide-ness of amino acid sequences. The novel signal peptides selected in this study increased Fc-fusion protein production in CHO cells by increasing specific protein productivity, whereas they did not negatively affect cell growth. Particularly, the SP-#149 clone showed the highest q p , 0.73 ± 0.01 pg/cell/day from day 1 to day 4, representing a 1.47-fold increase over the native signal peptide in a serum-free suspension culture mode. In addition, replacing native signal peptide with the novel signal peptides did not significantly affect sialylated N-glycan formation, N-terminal cleavage pattern, and biological function of Fc-fusion protein produced in CHO cells. The overall results indicate the utility of a novel in vitro screening system for synthetic signal peptide for mammalian cell-based protein production. KEY POINTS: • An in vitro screening system for synthetic signal peptide in mammalian cells was established • This system combined a degenerate codon-based library, site-specific integration, and a FACS-based detection assay • The novel signal peptides selected in this study could increase Fc-fusion protein production in mammalian cells., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Antimicrobial peptides (AMPs), as an important part of the innate immune system of an organism, is a kind of promising drug candidate for novel antibiotics due to their unique antibacterial mechanism. However, the discovery of novel AMPs is facing a great challenge due to the complexity of systematic experiments and the poor predictability of antimicrobial activity. Here, a novel and comprehensive screening system, the Multiple Descriptor Multiple Strategy (MultiDS), was proposed based on 59 physicochemical and structural parameters, three strategies, and four algorithms for the mining of α-helical AMPs. This approach was applied to mine the encrypted peptide antibiotics from the global human genome, including introns and exons. A library of approximately 70 billion peptides with 15-25 amino acid residues was screened by the MultiDS system and generated a list of peptides with the Multiple Descriptor Index (MD index) scores, which was the core part of the MultiDS system. Sixty peptides with top MD scores were chemically synthesized and experimentally tested their antimicrobial activity against 10 kinds of Gram-positive bacteria, Gram-negative bacteria (including drug-resistant pathogens). A total of fifty-nine out of 60 (98.3%) peptides exhibited antimicrobial activity (MIC ≤ 64 μg/mL), and 24 out of 60 (40%) peptides showed high activity (MIC ≤ 2 μg/mL), validating the MultiDS system was an effective and predictive screening tool with high hit rate and superior antimicrobial activity. For further investigation, AMPs S1, S2, and S3 with the highest MD scores were used to treat the skin infection mouse models in vivo caused by Escherichia coli , drug-resistance Escherichia coli , and Staphylococcus aureus , respectively. All of S1, S2, and S3 showed comparable therapeutic effects on promoting infection healing to or even better than the positive drug levofloxacin. A mechanism study discovered that rapid bactericidal action was caused by cell membrane disruption and content leakage. The MultiDS system not only provides a high-throughput approach that allows for the mining of candidate AMPs from the global genome sequence but also opens up a new route to accelerate the discovery of peptide antibiotics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Wang, Zhang, Zhang, Wu and Zhang.)

Background: Alzheimer disease (AD) and other types of dementia are now considered one of the world's most pressing health problems for aging people worldwide. It was the seventh-leading cause of death, globally, in 2019. With a growing number of patients with dementia and increasing costs for treatment and care, early detection of the disease at the stage of mild cognitive impairment (MCI) will prevent the rapid progression of dementia. In addition to reducing the physical and psychological stress of patients' caregivers in the long term, it will also improve the everyday quality of life of patients., Objective: The aim of this study was to design a digital screening system to discriminate between patients with MCI and AD and healthy controls (HCs), based on the Rey-Osterrieth Complex Figure (ROCF) neuropsychological test., Methods: The study took place at National Taiwan University between 2018 and 2019. In order to develop the system, pretraining was performed using, and features were extracted from, an open sketch data set using a data-driven deep learning approach through a convolutional neural network. Later, the learned features were transferred to our collected data set to further train the classifier. The first data set was collected using pen and paper for the traditional method. The second data set used a tablet and smart pen for data collection. The system's performance was then evaluated using the data sets., Results: The performance of the designed system when using the data set that was collected using the traditional pen and paper method resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.913 (SD 0.004) when distinguishing between patients with MCI and HCs. On the other hand, when discriminating between patients with AD and HCs, the mean AUROC was 0.950 (SD 0.003) when using the data set that was collected using the digitalized method., Conclusions: The automatic ROCF test scoring system that we designed showed satisfying results for differentiating between patients with AD and MCI and HCs. Comparatively, our proposed network architecture provided better performance than our previous work, which did not include data augmentation and dropout techniques. In addition, it also performed better than other existing network architectures, such as AlexNet and Sketch-a-Net, with transfer learning techniques. The proposed system can be incorporated with other tests to assist clinicians in the early diagnosis of AD and to reduce the physical and mental burden on patients' family and friends., (©Wen-Ting Cheah, Jwu-Jia Hwang, Sheng-Yi Hong, Li-Chen Fu, Yu-Ling Chang, Ta-Fu Chen, I-An Chen, Chun-Chen Chou. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 09.03.2022.)

Background: The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary., Objective: We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients., Methods: We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects., Results: The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification., Conclusion: The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The SARS-CoV-2 or shortly COVID-19, is a viral disease which causes serious lung fever and hugely impacts different body parts from mild to critical depending on tolerant immune system. As the virus multiplies through human-to-human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Viral spreading of this virus can only be prevented by early detection of positive cases and to treat infected patients as quickly as possible. As many businesses, banks, gymnasiums, and stores etc., are using temperature screening as the primary step to assess for possible COVID-19 infection. Moreover, the proper hand sanitization is the very effective method to limit the outspread of this virus. This paper proposes the design and development of a fully automated low-cost portable electronic system in the form of a robot named CovBot that can be installed in the above-mentioned places by incorporating the mechanisms to automatically detect the body temperature, store the details directly to cloud so as to get the data latter by the authorities, to control/restrict the entry, a hand sanitization dispenser unit, auto alert to refill the sanitizer, a mobile display unit etc. Whole system can be managed by a mobile application. The system is controlled using an Arduino-Uno development board. The mobile and the microcontroller system is wirelessly communicated and that to cloud is done by IoT facility. Once this system is implemented, the primary concern and the initial screening associated to COVID-19 can be fully resolved. Comparing to other systems CovBot is cost effective and can be easily installed and operated., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Artificial Intelligence & Energy Systems.)

Amylin (hIAPP) amyloid formation plays an important role in the pathogenesis of type 2 diabetes (T2D), which makes it a promising therapeutic target for T2D. In this study, we established a screening tool for identifying chemicals affecting hIAPP amyloid formation based on a reported genetic tool, which constantly tracks protein aggregates in Saccharomyces cerevisiae. In order to obtain the hIAPP with better aggregation ability, the gene of hIAPP was tandemly ligated to create 1×, 2×, 4× or 6×-hIAPP expressing strains. By measuring the cell density and fluorescence intensity of green fluorescent protein (GFP) regulated by the aggregation status of hIAPP, it was found that four intramolecular ligated hIAPP (4×hIAPP) could form obvious amyloids with mild toxicity. The validity and reliability of the screening tool were verified by testing six reported hIAPP inhibitors, including curcumin, epigallocatechin gallate and so on. Combined with surface plasmon resonance (SPR) and the screening tool, which could be a screening system for hIAPP inhibitors, we found that crocin specifically binds to hIAPP and acts inhibit amyloid formation of hIAPP. The effect of crocin was further confirmed by Thioflavin T (ThT) fluorescence and transmission electron microscopy (TEM) analysis. Thus, a screening system for hIAPP amyloid inhibitors and a new mechanism of crocin on anti-T2D were obtained as a result of this study., (© 2021 Wiley-VHCA AG, Zurich, Switzerland.)

Regulating morphology engineering and fermentation of Aspergillus oryzae makes it possible to increase the titer of L-malate. However, the existing L-malate-producing strain has limited L-malate production capacity and the fermentation process is insufficiently mature, which cannot meet the needs of industrial L-malate production. To further increase the L-malate production capacity of A. oryzae, we screened out a mutant strain (FMME-S-38) that produced 79.8 g/L L-malate in 250-mL shake flasks, using a newly developed screening system based on colony morphology on the plate. We further compared the extracellular nitrogen (N1) and intracellular nitrogen (N2) contents of the control and mutant strain (FMME-S-38) to determine the relationship between the curve of nitrogen content (N1 and N2) and the L-malate titer. This correlation was then used to optimize the conditions for developing a novel nitrogen supply strategy (initial tryptone concentration of 6.5 g/L and feeding with 3 g/L tryptone at 24 h). Fermentation in a 7.5-L fermentor under the optimized conditions further increased the titer and productivity of L-malate to 143.3 g/L and 1.19 g/L/h, respectively, corresponding to 164.9 g/L and 1.14 g/L/h in a 30-L fermentor. This nitrogen regulation-based strategy cannot only enhance industrial-scale L-malate production but also has generalizability and the potential to increase the production of similar metabolites.Key Points• Construction of a new screening system based on colony morphology on the plate.• A novel nitrogen regulation strategy used to regulate the production of L-malate.• A nitrogen supply strategy used to maximize the production of L-malate.

A novel surface plasmon resonance-based P-gp ligand screening system (SPR-PLSS) combined with lentiviral particle (LVP) stabilization strategy was constructed to screen out potential P-gp inhibitors from natural products. Firstly, we constructed LVPs with high and low expression levels of P-gp. The LVPs can ensure the natural conformation of P-gp based on the principle that LVPs germinated from packaging cells will contain cell membrane fragments and P-gp they carry. Then the LVPs with high P-gp expression for active channel and LVPs with low P-gp expression for reference channel were immobilized on CM5 chip respectively. The affinity detection was thus carried out with the signal reduction on the two channels. The P-gp inhibitors, Valspodar (Val) and cyclosporin (CsA), as positive compounds, were detected to characterize the chip's activity, and the K D of Val and CsA were 14.09 μM and 16.41 μM, respectively. Forty compounds from natural product library were screened using the SPR CM5 chip, and magnolol (Mag), honokiol (Hon), and resveratrol (Res) were screened out as potential P-gp ligands, showing a significant response signal. This work presented a novel P-gp ligand screening system based on LVP-immobilized biosensor to rapidly screen out P-gp ligands from natural product library. Compared with traditional cell experiments which the screening time may take up to several days, our method only takes several hours. Furthermore, this study has also provided solid evidences to support that some complicated membrane proteins would apply to the lentivirus-based SPR screening system.

Cyclic peptides are an attractive modality for the development of therapeutics and the identification of functional cyclic peptides that contribute to novel drug development. The peptide array is one of the optimization methods for peptide sequences and also useful to understand sequence-function relationship of peptides. Cell adherent cyclic NGR peptide which selectively binds to the aminopeptidase N (APN or CD13) is known as an attractive tumor marker. In this study, we designed and screened a library of different length and an amino acid substitution library to identify stronger cell adhesion peptides and to reveal that the factor of higher binding between CD13 and optimized cyclic peptides. Additionally, we designed and evaluated 192 peptide libraries using eight representative amino acids to reduce the size of the library. Through these optimization steps of cyclic peptides, we identified 23 peptides that showed significantly higher cell adhesion activity than cKCNGRC, which was previously reported as a cell adhesion cyclic peptide. Among them, cCRHNGRARC showed the highest activity, that is, 1.65 times higher activity than cKCNGRC. An analysis of sequence and functional data showed that the rules which show higher cell adhesion activity for the three basic cyclic peptides (cCX 1 HNGRHX 2 C, cCX 1 HNGRAX 2 C, and cCX 1 ANGRHX 2 C) are related with the position of His residues and cationic amino acids., (© 2020 European Peptide Society and John Wiley & Sons, Ltd.)

Various stresses (high temperature, starvation, or sublethal Cryptococcal infection) increased the susceptibility of silkworms to bacterial infection by up to 100-fold, confirming the stress-induced immunosuppression reported in a range of species. When the silkworm was injected with a steroidal drug, betamethasone (1 mg/larva), the susceptibility of the silkworm to bacterial infection increased about 100-fold. This indicates that the immune function of the silkworm can be suppressed by a known compound that shows immunosuppressive effects in humans. We further tested the immunosuppressive effect of the culture supernatants (acetone extracts) of soil bacteria, and 24 out of 193 isolates showed the immunosuppressive activity. These results suggest that it is possible to search for immunosuppressive agents targeting innate immunity by using a silkworm bacterial infection model as a screening system, and that there may be candidate compounds for immunosuppressive agents among the substances produced by soil bacteria.

Resistance management plays a key role in modern plant protection. There is a growing need to identify new fungicide targets and new modes of action. In this context, it is also mandatory to find new compounds acting on successful target locations. For the latter, so-called target-site-specific test systems emerged to search for inhibitors. Most of them are based on in vitro assays, in which interaction between a compound and a purified target protein is demonstrated. Consequently, getting essential information about potentially toxic effects in the living cell or in the whole organism is not possible. Thus, we present a fluorescent-labelled mutant strain of the rice blast fungus Magnaporthe oryzae as a rapid tool for fluorescence-based identification and visualization of fungicides in vivo with the mode of action in the high osmolarity glycerol (HOG)-signaling pathway. The HOG pathway represents an excellent target for antifungal agents such as the phenylpyrrole fungicides, since almost no relevant resistances have occurred to date, despite 30 years of extensive usage of this fungicide class.

In nature, plants have evolved a myriad of preformed and induced defenses to protect themselves from microbes. Upon microbial infection, the recognition of the microbe-associated molecular patterns (MAMPs) by the pattern recognition receptors (PRRs) triggers the first stage of defense response (Dodds and Rathjen, Nat Rev Genet 11:539-548, 2010). However, in order to develop microbial delivery, effectors target PRRs for deregulating immune responses and facilitating host colonization (Thomma et al., Plant Cell 23:4-15, 2011). Here, we contribute a protocol for the screening system of Magnaporthe oryzae effectors and construct a fluorescent system to trace secretory proteins in the sheath infection samples. Using the tobacco rattle virus (TRV) system, the proteins including LysM, Chitin, Cutinase, and CFEM domains were selected and divided into two kinds according to the results of cell death induced or inhibited test in Nicotiana benthamiana. Then, candidate effectors can be deleted or overexpressed in M. oryzae. The barley or rice infection with M. oryzae, rice leaf sheath inoculation, and subcellular localization during infection can be performed to explore the functions of these effectors.

We developed a method for efficiently activating functional peptides with a large structural contribution using the peptide-searching method with machine learning. The physicochemical properties of the amino acids were employed as variables. As a model peptide, we used GHWYYRCW, which is a functional peptide that inhibits α-amylase derived from human pancreatic juice. First, training data were acquired. A total of 153 peptides were prepared in which 1 amino acid in GHWYYRCW was replaced to construct a 1-amino acid substitution coverage peptide library. The inhibitory activity of each peptide against α-amylase and α-glucosidase was evaluated. Second, random forest (RF) regression analysis was performed using 120 variables, and the enzyme inhibitory activity of the peptide was related to the physicochemical properties. The constructed model had many features describing the charge of the amino acid (isoelectric point and p K 2 ). Then, high inhibitory (HI) peptides were predicted using a library of peptides with 2- or 3-amino acid substitution as test data, which were called HI2 and HI3 peptides. As results, the first or seventh amino acid of the HI2 peptide was replaced with Arg, Trp, or Tyr. We found that all 30 HI2 peptides had significantly higher activity than the original sequence (100%) and 26 of the 30 HI3 peptides were significantly active (86.7%). However, the actual inhibitory activity of the HI3 peptides was improved to a lesser extent. The docking simulation clarified that the CDOCKER energy decrease was roughly correlated with the inhibitory activity. The machine learning-based predictive model was a promising tool for design of substituted peptides with high activity values, and it was assumed that the advanced model that forecasts the interaction index such as the CDOCKER energy substituting for the inhibitory activity would be used to design HI peptides, even in the case of the HI3 peptides.

Intracellular functional peptides with important roles in cells have been widely studied as regulators of cellular activity. Previously, we proposed a screening system for identifying intracellular functional peptides, which can be utilized to synthesize various cell-penetrating peptide (CPP)-functional peptide complexes, and the intracellular functions of the peptides can then be determined using various cell based assays. We demonstrated that the activity of a cell death-inducing peptide was increased by an amino acid substitution. However, covalent conjugation of CPPs with functional peptides may disrupt the biological characteristics of the functional peptides. In the present study, we developed a novel screening system for intracellular functional peptides combined with a dissociation system after delivery inside cells to eliminate the influence of CPPs. To construct this system, we developed a method for synthesizing CPP-functional peptide hetero-dimers containing a disulfide bond that is cleaved by the intracellular reducing environment. The system used Fmoc-Lys(ivDde)-OH to synthesize CPP and functional peptides in one molecule to selectively form a disulfide bond of hetero-dimeric peptides. Furthermore, we evaluated a single amino acid substitution library of the cell death inducing peptide (WELVVLGKL). We obtained 6 peptides with higher activity than the original peptide. Our results suggest that our new screening system can be eliminate the influence of CPPs and is useful for peptide screening inside cells., (Copyright © 2019 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.)

Introduction: A key feature of the 'One Health' concept pertains to the design of novel point of care systems for largescale screening of health of the population residing in resource-limited areas of low- and middle-income countries with a view to obtaining data at a community level as a rationale to achieve better public health outcomes. The physical properties of blood are different for different samples. Our study involved the development of an innovative system architecture based upon the physical properties of blood using automated classifiers to enable large-scale screening of the health of the population living in resource-limited settings. Methods: The proposed system consisted of a simple, robust and low-cost sensor with capabilities to sense and measure even the minute changes in the physical properties of blood samples. In this system, the viscosity of blood was derived from a power-law model coupled with the Rabinowitsch-Mooney correction for non-Newtonian shear rates developed in a steady laminar Poiseuille flow. Surface tension was measured by solving the Young-Laplace equation for pendant drop shape hanging on a vertical needle. An anticipated outcome of this study would be the development of a novel automated classifier based upon the rheological attributes of blood. This automated classifier would have potential application in evaluating the health status of a population at regional and global levels. Results: The proposed system was used to measure the physical properties of various samples like normal, tuberculous and anemic blood samples. The results showed that the physical properties of these samples were different as compared to normal blood samples. The major advantage of this system was low-cost, as well as its simplicity and portability. Conclusion: In this work, we proposed making a case for the validation of a low-cost version of a microfluidic system capable of scanning large populations for a variety of diseases as per the WHO mandate of "One Health"., (© 2020 The Author(s).)

Z-DNA, which is left-handed double-stranded DNA, is involved in various cellular processes. However, its biological roles have not been fully evaluated due to the lack of tools available that can control the precise conformational change to Z-DNA in vitro and in vivo. Therefore, the need for identifying new Z-DNA inducers is high. We developed an assay system to monitor the conformational change in DNA utilizing the fluorescence of SYBR green I integrated into a double-stranded oligonucleotide. By applying this assay to screen for compounds that induce the B-DNA to Z-DNA transition, we identified the natural compound aklavin as a novel Z-DNA inducer., (© 2019 Federation of European Biochemical Societies.)

Food processing technology such as protein hydrolysis using proteases has been receiving a lot of attention, and it is important to accurately understand the cleavage specificity of each protease for selecting a protease suited to aims. Although numerous methods have been reported to reveal the substrate specificity of proteases, there is no method to evaluate simply, quickly, reasonably, and accurately. This study set out to devise Pep-MS assay, a novel assay system that can be used to comprehensively clarify positions at which proteases cleave, by combining a mass spectrometer and a photo-cleavable peptide array. First, we evaluated peptide array corresponding to the primary sequences of α S1 -casein, α S2 -casein and β-casein with trypsin to verify the accuracy of the Pep-MS assay. The evaluation of cleavage positions by the trypsin protease reagent using the Pep-MS assay resulted in a matching rate of about 96.8% to rational cleavage positions. Next, we confirmed the cleavage positions in α S2 -casein or β-lactoglobulin by an industrial bacterial protease from Bacillus subtilis at some protease reaction temperatures or reaction times. The Pep-MS assay clarified the differences in the cleavage patterns due to the reaction temperature, and the change in the cleavage strength with the reaction time. Pep-MS assay is a promising method for evaluating the substrate specificity of proteases, which will be useful to find effective production conditions for functional peptide from foods and effective hydrolysis conditions for decreasing allergen of food proteins., (Copyright © 2019 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.)

Evaluating the discrimination ability of a deep convolution neural network for ultrawide-field pseudocolor imaging and ultrawide-field autofluorescence of retinitis pigmentosa. In total, the 373 ultrawide-field pseudocolor and ultrawide-field autofluorescence images (150, retinitis pigmentosa; 223, normal) obtained from the patients who visited the Department of Ophthalmology, Tsukazaki Hospital were used. Training with a convolutional neural network on these learning data objects was conducted. We examined the K -fold cross validation ( K = 5). The mean area under the curve of the ultrawide-field pseudocolor group was 0.998 (95% confidence interval (CI) [0.9953-1.0]) and that of the ultrawide-field autofluorescence group was 1.0 (95% CI [0.9994-1.0]). The sensitivity and specificity of the ultrawide-field pseudocolor group were 99.3% (95% CI [96.3%-100.0%]) and 99.1% (95% CI [96.1%-99.7%]), and those of the ultrawide-field autofluorescence group were 100% (95% CI [97.6%-100%]) and 99.5% (95% CI [96.8%-99.9%]), respectively. Heatmaps were in accordance with the clinician's observations. Using the proposed deep neural network model, retinitis pigmentosa can be distinguished from healthy eyes with high sensitivity and specificity on ultrawide-field pseudocolor and ultrawide-field autofluorescence images., Competing Interests: Hiroki Enno is an employee of Rist Inc.

Peroxisome proliferator-activated receptors (PPARs) belong to the nuclear hormone receptor superfamily and include three subtypes (PPARα, PPARδ, and PPARγ). They regulate gene expression in a ligand-dependent manner. PPARα plays an important role in lipid metabolism. PPARγ is involved in glucose metabolism and is a potential therapeutic target in Type 2 diabetes. PPARδ ligands are candidates for the treatment of metabolic disorders. Thus, the detection of PPAR ligands may facilitate the treatment of various diseases. In this study, to identify PPAR ligands, we engineered reporter cell lines that can be used to quantify PPARγ and PPARδ activity. We evaluated several known ligands using these reporter cell lines and confirmed that they are useful for PPAR ligand detection. Furthermore, we evaluated extracts of approximately 200 natural resources and found various extracts that enhance reporter gene activity. Finally, we identified a main alkaloid of the Evodia fruit, evodiamine, as a PPARγ activator using this screening tool. These results suggest that the established reporter cell lines may serve as a useful cell-based screening tool for finding PPAR ligands to ameliorate metabolic syndromes.

Microbial L-malate production from renewable feedstock is a promising alternative to petroleum-based chemical synthesis. However, high L-malate production of Aspergillus oryzae was achieved to date using organic nitrogen, with inorganic nitrogen still unable to meet industrial applications. In the current study, we constructed a screening system and nitrogen supply strategy to improve L-malate production with ammonium sulphate [(NH 4 ) 2 SO 4 ] as the sole nitrogen source. First, we generated and identified a high-producing mutant FMME218-37, which stably boosted L-malate production from 30.73 to 78.12 g/L, using a combined screening system with morphological characteristics. Then, by analyzing the fermentation parameters and physiological characteristics, we further speculated the key factor was the unbalance of carbon and nitrogen absorption. Finally, the titer and productivity of L-malate was increased to 95.2 g/L and 0.57 g/(L h) by regulating the nitrogen supply module to balance carbon and nitrogen absorption, which represented the highest level in A. oryzae with (NH 4 ) 2 SO 4 as nitrogen source achieved to date. Moreover, our findings using a low-cost substrate may lead to building an economical cell factory of A. oryzae for L-malate production.

Hexoses are the major source of energy and carbon skeletons for biosynthetic processes in all kingdoms of life. Their cellular uptake is mediated by specialized transporters, including glucose transporters (GLUT, SLC2 gene family). Malfunction or altered expression pattern of GLUTs in humans is associated with several widespread diseases including cancer, diabetes and severe metabolic disorders. Their high relevance in the medical area makes these transporters valuable drug targets and potential biomarkers. Nevertheless, the lack of a suitable high-throughput screening system has impeded the determination of compounds that would enable specific manipulation of GLUTs so far. Availability of structural data on several GLUTs enabled in silico ligand screening, though limited by the fact that only two major conformations of the transporters can be tested. Recently, convenient high-throughput microbial and cell-free screening systems have been developed. These remarkable achievements set the foundation for further and detailed elucidation of the molecular mechanisms of glucose transport and will also lead to great progress in the discovery of GLUT effectors as therapeutic agents. In this mini-review, we focus on recent efforts to identify potential GLUT-targeting drugs, based on a combination of structural biology and different assay systems.

Split-luciferase techniques are widely used to detect protein-protein interaction and bioactive small molecules including some hormones and vitamins. Previously, we successfully expressed chimeric proteins of luciferase and the ligand binding domain (LBD) of the vitamin D receptor (VDR), LucC-LBD-LucN in COS-7 cells. The LucC-LBD-LucN biosensor was named split-luciferase vitamin D biosensor (SLDB). This biosensor can detect and discriminate between VDR agonists and antagonists in mammalian cells. In this study, we established an in vitro screening system for VDR ligands using the SLDB proteins expressed in Escherichia coli (E. coli) cells. Our in vitro screening system using cell lysate of recombinant E. coli cells could be completed within 30min, and its activity was unchanged after 10 freeze-thaw cycles. This highly sensitive and convenient system would be quite useful to screen VDR ligands with therapeutic potential for various bone-related diseases, age-related cognitive disorders, cancer, and immune disorders. In addition, our system might be applicable to diagnostic measurement of serum concentrations of 25-hydroxyvitamin D 3 and 1α,25-dihydroxyvitamin D 3 ., (Copyright © 2017. Published by Elsevier Ltd.)

This paper makes a brief review of several database structures of Computer-Based Speech Therapy (CBST) systems and solutions and describes the screening method, an experimental study conducted to validate the screening algorithm and a database structure for the Information Entropy-Based Sound Speech Disorder (SSD) Screening System aimed at by our research project. The final part briefly presents the essential design criteria and further development.

The apparent increase in hormone-induced cancers and disorders of the reproductive tract has led to a growing demand for new technologies capable of screening xenoestrogens. We reported an estrogen receptor (ER)-based reusable fiber biosensor for facile screening estrogenic compounds in environment. The bioassay is based on the competition of xenoestrogens with 17β-estradiol (E 2 ) for binding to the recombinant receptor of human estrogen receptor α (hERα) protein, leaving E 2 free to bind to fluorophore-labeled anti-E 2 monoclonal antibody. Unbound anti-E 2 antibody then binds to the immobilized E 2 -protein conjugate on the fiber surface, and is detected by fluorescence emission induced by evanescent field. As expected, the stronger estrogenic activity of xenoestrogen would result in the weaker fluorescent signal. Three estrogen-agonist compounds, diethylstilbestrol (DES), 4-n-nonylphenol (NP) and 4-n-octylphenol (OP), were chosen as a paradigm for validation of this assay. The rank order of estrogenic potency determined by this biosensor was DES>OP>NP, which were consistent with the published results in numerous studies. Moreover, the E 2 -protein conjugate modified optical fiber was robust enough for over 300 sensing cycles with the signal recoveries ranging from 90% to 100%. In conclusion, the biosensor is reusable, reliable, portable and amenable to on-line operation, providing a facile, efficient and economical alternative to screen potential xenoestrogens in environment., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Intracellular functional peptides that play a significant role inside cells have been receiving a lot of attention as regulators of cellular activity. Previously, we proposed a novel screening system for intracellular functional peptides; it combined a photo-cleavable peptide array system with cell-penetrating peptides (CPPs). Various peptides can be delivered into cells and intracellular functions of the peptides can be assayed by means of our system. The aim of the present study was to demonstrate that the proposed screening system can be used for assessing the intracellular activity of peptides. The cell death-inducing peptide (LNLISKLF) identified in a mitochondria-targeting domain (MTD) of the Noxa protein served as an original peptide sequence for screening of peptides with higher activity via modification of the peptide sequence. We obtained 4 peptides with higher activity, in which we substituted serine (S) at the fifth position with phenylalanine (F), valine (V), tryptophan (W), or tyrosine (Y). During analysis of the mechanism of action, the modified peptides induced an increase in intracellular calcium concentration, which was caused by the treatment with the original peptide. Higher capacity for cell death induction by the modified peptides may be caused by increased hydrophobicity or an increased number of aromatic residues. Thus, the present work suggests that the intracellular activity of peptides can be assessed using the proposed screening system. It could be used for identifying intracellular functional peptides with higher activity through comprehensive screening., (Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.)

Screening of tissue samples for glycogen is very important in assessing the ageing, but also the state of health of the tissue. Therefore, two needle stochastic sensors based on maltodextrins presenting different dextrose equivalence (DE) MD-m (DE 13.0-17.0), and MD-M (DE 16.5-19.5) immobilized in diamond paste (obtained from synthetic diamond and paraffin oil) were designed and characterized. These stochastic sensors were used reliable for both qualitative and quantitative analysis for the assay of glycogen in tissue samples with limits of determination as low as 1fmol L -1 ., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Tobacco smoking is a public health problem, with ∼5 million deaths per year, representing a heavy burden for many countries. No effective therapeutic strategies are currently available for nicotine addiction, and it is therefore crucial to understand the etiological and pathophysiological factors contributing to this addiction. The neuronal α5 nicotinic acetylcholine receptor (nAChR) subunit is critically involved in nicotine dependence. In particular, the human polymorphism α5D398N corresponds to the strongest correlation with nicotine dependence risk found to date in occidental populations, according to meta-analysis of genome-wide association studies. To understand the specific contribution of this subunit in the context of nicotine addiction, an efficient screening system for native human nAChRs is needed. We have differentiated human induced pluripotent stem (iPS) cells into midbrain dopaminergic (DA) neurons and obtained a comprehensive characterization of these neurons by quantitative RT-PCR. The functional properties of nAChRs expressed in these human DA neurons, with or without the polymorphism in the α5 subunit, were studied with the patch-clamp electrophysiological technique. Our results in human DA neurons carrying the polymorphism in the α5 subunit showed an increase in EC 50 , indicating that, in the presence of the polymorphism, more nicotine or acetylcholine chloride is necessary to obtain the same effect. This human cell culturing system can now be used in drug discovery approaches to screen for compounds that interact specifically with human native and polymorphic nAChRs.-Deflorio, C., Blanchard, S., Carisì, M. C., Bohl, D., Maskos, U. Human polymorphisms in nicotinic receptors: a functional analysis in iPS-derived dopaminergic neurons., (© FASEB.)

During the past years customized gene network design has become of tremendous interest among various disciplines in life science. The identification of artificial genetic elements sensitive to internal or external stimuli constitutes the foundation for the design and realization of conditional gene expression systems. Typically, strategies involving selection or screening steps are employed alongside approaches focusing on rational design to select for the desired functionality of a given element. Here we present a fluorescence-based in vivo screening approach that combines an initial in vitro selection with subsequent extensive screening steps and a final rational design to identify RNA based regulators in baker's yeast. These artificial RNA regulators, termed synthetic riboswitches, are derived from RNA aptamers. Our method allows for the separation of aptamers featuring the potential to be transformed into a riboswitch from those inherently unable to confer control over gene expression. The system may be applied to virtually all existing aptamer-ligand pairs and as such presents a powerful means to enhance the setup of switchable genetic circuits., (Copyright © 2016. Published by Elsevier Inc.)

After extensive research on radiochemotherapy, 5-year survival rates of children with high risk neuroblastoma still do not exceed 50%, owing to adverse side-effects exemplified by doxorubicin-induced cardiomyopathy. A promising new approach is the combination of conventional therapies with specific modulation of cell signaling pathways promoting therapeutic resistance, such as inhibition of aberrant kinase activity or re-expression of silenced tumor suppressor genes by means of chromatin remodeling. In this regard, we established a system that allows to identify potential drug targets as well as to validate respective candidate inhibitors in high-risk neuroblastoma model cell lines. Cell culture, drug exposure, shRNA-mediated knockdown and phenotype analysis are integrated into an efficient and versatile single well-based protocol. By utilizing this system, we assessed RG108, SGI-1027 and nanaomycin A, three novel DNA methyltransferase inhibitors that have not been tested in neuroblastoma cell lines so far, for their potential of synergistic anti-tumor activity in combination with doxorubicin. We found that, similarly to azacytidine, SGI-1027 and nanaomycin A mediate synergistic growth inhibition with doxorubicin independently of N-Myc status. However, they display high cytotoxicity but lack global DNA demethylation activity. Secondly, we conducted a lentiviral shRNA screen of F-box proteins, key regulators of protein stability, and identified Fbxw11/β-TrCP2 as well as Fbxo5/Emi1 as potential therapeutic targets in neuroblastoma. These results complement existing studies and underline the reliability and versatility of our single well-based protocol.

The blood circulatory system links all organs from one to another to support and maintain each organ's functions consistently. Therefore, blood vessels have been considered as a vital unit. Engineering perfusable functional blood vessels in vitro has been challenging due to difficulties in designing the connection between rigid macroscale tubes and fragile microscale ones. Here, we propose a generalizable method to engineer a "long" perfusable blood vessel network. To form millimeter-scale vessels, fibroblasts were co-cultured with human umbilical vein endothelial cells (HUVECs) in close proximity. In contrast to previous works, in which all cells were permanently placed within the device, we developed a novel method to culture paracrine factor secreting fibroblasts on an O-ring-shaped guide that can be transferred in and out. This approach affords flexibility in co-culture, where the effects of secreted factors can be decoupled. Using this, blood vessels with length up to 2 mm were successfully produced in a reproducible manner (>90%). Because the vessels form a perfusable network within the channel, simple links to inlets and outlets of the device allowed connections to the outside world. The robust and reproducible formation of in vitro engineered vessels can be used as a module to link various organ components as parts of future body-on-a-chip applications., (© 2014 Society for Laboratory Automation and Screening.)

The obesogen hypothesis states that together with an energy imbalance between calories consumed and calories expended, exposure to environmental compounds early in life or throughout lifetime might have an influence on obesity development. In this work, we propose a new approach for obesogen screening, i.e., the use of transcriptomics in the 3T3-L1 pre-adipocyte cell line. Based on the data from a previous study of our group using a lipid accumulation based adipocyte differentiation assay, several human-relevant obesogenic compounds were selected: reference obesogens (Rosiglitazone, Tributyltin), test obesogens (Butylbenzyl phthalate, butylparaben, propylparaben, Bisphenol A), and non-obesogens (Ethylene Brassylate, Bis (2-ethylhexyl)phthalate). The high stability and reproducibility of the 3T3-L1 gene transcription patterns over different experiments and cell batches is demonstrated by this study. Obesogens and non-obesogen gene transcription profiles were clearly distinguished using hierarchical clustering. Furthermore, a gradual distinction corresponding to differences in induction of lipid accumulation could be made between test and reference obesogens based on transcription patterns, indicating the potential use of this strategy for classification of obesogens. Marker genes that are able to distinguish between non, test, and reference obesogens were identified. Well-known genes involved in adipocyte differentiation as well as genes with unknown functions were selected, implying a potential adipocyte-related function of the latter. Cell-physiological lipid accumulation was well estimated based on transcription levels of the marker genes, indicating the biological relevance of omics data. In conclusion, this study shows the high relevance and reproducibility of this 3T3-L1 based in vitro toxicogenomics tool for classification of obesogens and biomarker discovery. Although the results presented here are promising, further confirmation of the predictive value of the set of candidate biomarkers identified as well as the validation of their clinical role will be needed., (© The Author 2014. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The availability of a useful tool for simple and timely detection of the most important virulent varieties of Escherichia coli is indispensable. To this end, bacterial DNA pools which had previously been categorized were obtained from isolated colonies as well as selected in terms of utilized phenotype; the pools were assessed by two PCR Multiplex for the detection of virulent E. coli eaeA, bfpA, stx1, stx2, ipaH, ST, LT, and aatA genes, with the 16S gene used as DNA control. The system was validated with 66 fecal samples and 44 wastewater samples. At least one positive isolate was detected by a virulent gene among the 20 that were screened. The analysis of fecal samples from children younger than 6 years of age detected frequencies of 25% LT positive strains, 8.3% eae, 8.3% bfpA, 16.7% ipaH, as well as 12.5 % aatA and ST. On the other hand, wastewater samples revealed frequencies of 25.7% eaeA positive, 30.3% stx1, 15.1% LT and 19.7% aatA. This study is an initial step toward carrying out epidemiological field research that will reveal the presence of these bacterial varieties.