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IntroductionNeonatal 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.ObjectiveOur 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 designOur 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.ResultsThe 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.ConclusionLogistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.

The current work demonstrates the fabrication of kaolin supported Au nanoparticles (Au NPs-kaolin) mediated by Thymbra spicata extract as green reductant and capping agent without any toxic reagent. Physicochemical characteristics of the said nanocomposite were elucidated by field emission scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, elemental mapping, X-ray diffraction and inductively coupled plasma techniques. The figures of the TEM display the black dots signifying Au NPs being dispersed over the kaolin surface. Size of spherical Au NPs are around 10–15 nm. In the in vivo, we established a rat calvaria defect model using a combination of collagen scaffold and nanoparticle. The experimental group was divided into three classifications: control, collagen matrix and nanoparticle with collagen. Histological analyses showed that nanoparticle increased bone formation activity when used in conjunction with collagen matrix. In the nanoparticle group, grafted materials were still present until 12 weeks after treatment, as evidenced by foreign body reactions showing multinucleated giant cells in chronic inflammatory vascular connective tissue. Other results revealed that the nanoparticle increased bone formation activity when used with collagen matrix. All groups showed almost the same histological findings until 7 weeks. In the experimental groups, new bone formation activity was found continuously up to 12 weeks.

Background: The COVID-19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID-19 quickly and accurately. Early identification of COVID-19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores based upon clinical and laboratory findings in patients screen for COVID-19 infection. Methodology: This study used a retrospective cohort design that involved 705 adults (= 18 y old) admitted in Dr. Sardjito Hospital and Dr. S. Hardjolukito Hospital. The patients' data collected included demographic characteristics, anamnesis on signs and symptoms, history of contact with COVID-19 patients, history of staying or visiting an endemic area, comorbidities, and laboratory and radiologic indicators. All variables with a P < 0.25 on the bivariate test were included in a univariable logistic regression. If the P < 0.05, the variable was included in the multivariable logistic regression with a P < 0.05 considered significant. Receiver Operating Characteristic (ROC) producing an area under the curve (AUC) with 95% confidence intervals (CIs) was used to assess discrimination power. Results: Two scores were generated; score in Model 1 consisted of clinical signs, basic laboratory indicators, and chest X-ray, and in Model 2 consisted of clinical signs, chest X-ray, basic and advanced laboratory indicators, including C-reactive protein (CRP), lactate dehydrogenase (LDH), albumin, and D-dimer. The ROC score of Model 1 was 0.801 (0.764-0. 838), which is considered good discrimination, and of Model 2 had excellent discrimination with a ROC of 0.858 (0.826-0. 891); the differences in the ROC of the two models was statistically significant (P = 0.03). The score of Model 1 more than 5 had 85% sensitivity and 61% specificity of positive COVID-19. A score of Model 2 more than 4 had 83% sensitivity and 72% specificity for diagnosing positive COVID-19. Conclusions: A simple score consisting of clinical symptoms and signs, and simple laboratory indicators can be used to screen for COVID-19 infection. [ABSTRACT FROM AUTHOR]

The current work demonstrates the fabrication of kaolin supported Au nanoparticles (Au NPs-kaolin) mediated by Thymbra spicata extract as green reductant and capping agent without any toxic reagent. Physicochemical characteristics of the said nanocomposite were elucidated by field emission scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, elemental mapping, X-ray diffraction and inductively coupled plasma techniques. The figures of the TEM display the black dots signifying Au NPs being dispersed over the kaolin surface. Size of spherical Au NPs are around 10–15 nm. In the in vivo, we established a rat calvaria defect model using a combination of collagen scaffold and nanoparticle. The experimental group was divided into three classifications: control, collagen matrix and nanoparticle with collagen. Histological analyses showed that nanoparticle increased bone formation activity when used in conjunction with collagen matrix. In the nanoparticle group, grafted materials were still present until 12 weeks after treatment, as evidenced by foreign body reactions showing multinucleated giant cells in chronic inflammatory vascular connective tissue. Other results revealed that the nanoparticle increased bone formation activity when used with collagen matrix. All groups showed almost the same histological findings until 7 weeks. In the experimental groups, new bone formation activity was found continuously up to 12 weeks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 qp, 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 [ABSTRACT FROM AUTHOR]

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 plantNaccumulation (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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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.

Globally, invasive plant-fueled wildfires have tremendous environmental, economical, and societal impacts, and the frequencies of wildfires and plant invasions are on an upward trend globally. Identifying which plant species tend to increase the frequency or severity of wildfire is important to help manage their impacts. We developed a screening system to identify introduced plant species that are likely to increase wildfire risk, using the Hawaiian Islands to test the system and illustrate how the system can be applied to inform management decisions. Expert-based fire risk scores derived from field experiences with 49 invasive species in Hawai′i were used to train a machine learning model that predicts expert fire risk scores from among 21 plant traits obtained from literature and databases. The model revealed that just four variables can identify species categorized as higher fire risk by experts with 90% accuracy, while low risk species were identified with 79% accuracy. We then used the predictive model to screen > 140 recently naturalized plants in Hawai′i to illustrate how the screening tool can be applied. The screening tool identified a managebly small set of species (6% of naturalizations in the last ~ 10 years) that are likely to pose a high fire risk and can be targeted for eradication or containment to reduce future wildfire risks. Because the screening system uses general plant traits that are likely relevant to fire risk in drylands around the world, it can likely be applied with minimal modification to other regions where invasive plants pose potential fire risks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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".

Various stresses (high temperature, starvation, or sublethal Cryptococcal infection) increased the susceptibility of silkworms to bacterial infection by up to 100-fold, confirming the stressinduced 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 KD 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. [ABSTRACT FROM AUTHOR]

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 (cCX1HNGRHX2C, cCX1HNGRAX2C, and cCX1ANGRHX2C) are related with the position of His residues and cationic amino acids. [ABSTRACT FROM AUTHOR]

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.)

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 middleincome 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". [ABSTRACT FROM AUTHOR]

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. Image 1 [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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 [(NH4)2SO4] 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 (NH4)2SO4 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Clostridium difficile infection (CDI) is an increasing global health threat and major worldwide cause of hospital-acquired diarrhea. The development of novel therapies to effectively treat this bacterial pathogen is an unmet clinical need. Here, we describe an image processing and classification algorithm that automatically identifies toxin-induced cytotoxicity to host cells based on characteristic morphological changes. This efficient and automatic algorithm can be incorporated into a screening platform to identify novel anti-toxin inhibitors of the C. difficile major virulence factors TcdA and TcdB, and contains the following steps: image enhancement, cell segmentation, and classification. We tested the algorithm on 504 images (containing 5096 cells) and achieved 93% sensitivity and 91% specificity, indicating that the proposed computational approach correctly classified most of the cells and provided reliable information for an effective screening platform. This algorithm achieved higher classification results compared to existing cell counter and analysis programs, scoring 92.6% accuracy. Compared to visual examination by a researcher, the algorithm significantly decreased classification time and identified toxin-induced cytotoxicity in an unbiased manner. Availability: Examples are available at home.agh.edu.pl/jaworek/CDI. [ABSTRACT FROM AUTHOR]

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 30 min, 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 . [ABSTRACT FROM AUTHOR]

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. [Display omitted] • CLC-MS screening of pharmaceuticals in waters and detection at concentrations lower than 10 ng mL−1. • Removal of a mixture of five drugs was evaluated using several EAOP. • Ibuprofen more recalcitrant than sulfadiazine, naproxen, diclofenac and ketoprofen. • Photo-electrolysis more efficient than photolysis and electrolysis. • Important differences in reactions pathways proposed for each technology studied. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 1 fmol L −1 . [ABSTRACT FROM AUTHOR]

The use of membrane reactors for enzymatic and co-factor regenerating reactions offers versatile advantages such as higher conversion rates and space-time-yields and is therefore often applied in industry. However, currently available screening and kinetics characterization systems are based on batch and fed-batch operated reactors and were developed for whole cell biotransformations rather than for enzymatic catalysis. Therefore, the data obtained from such systems has only limited transferability for continuous membrane reactors. The aim of this study is to evaluate and to improve a novel screening and characterization system based on the membrane reactor concept using the enzymatic hydrolysis of cellulose as a model reaction. Important aspects for the applicability of the developed system such as long-term stability and reproducibility of continuous experiments were very high. The concept used for flow control and fouling suppression allowed control of the residence time with a high degree of precision (±1% accuracy) in a long-term study (>100 h).

Clostridium difficile infection (CDI) is an increasing global health threat and major worldwide cause of hospital-acquired diarrhea. The development of novel therapies to effectively treat this bacterial pathogen is an unmet clinical need. Here, we describe an image processing and classification algorithm that automatically identifies toxin-induced cytotoxicity to host cells based on characteristic morphological changes. This efficient and automatic algorithm can be incorporated into a screening platform to identify novel anti-toxin inhibitors of the C. difficile major virulence factors TcdA and TcdB, and contains the following steps: image enhancement, cell segmentation, and classification. We tested the algorithm on 504 images (containing 5096 cells) and achieved 93% sensitivity and 91% specificity, indicating that the proposed computational approach correctly classified most of the cells and provided reliable information for an effective screening platform. This algorithm achieved higher classification results compared to existing cell counter and analysis programs, scoring 92.6% accuracy. Compared to visual examination by a researcher, the algorithm significantly decreased classification time and identified toxin-induced cytotoxicity in an unbiased manner. Availability: Examples are available at home.agh.edu.pl/jaworek/CDI.

A powerful tool in enzyme engineering is directed evolution. The design of a suitable screening system to identify enzyme variants with the feature desired is one of the most important points in directed evolution experiments. In this article, we developed and validated a four-step activity-based screening system with a high throughput rate to identify a variant of the metagenome β-galactosidase, called M1, with improved specific activity. An Escherichia coli host-vector system was chosen to realize a high production of the recombinant enzyme in microtiter plate format. Industrial-like conditions of milk processing in dairy industry (substrate lactose, solved in synthetic milk buffer, pH 6.75, and 8 °C) were used for the activity screening. With a coefficient of variance of 15.6 % and a Z′ factor value of 0.53, the assay was reproducible and suitable to identify enzyme variants with the requirement desired. A first-generation library of β-galactosidase variants, constructed by error-prone PCR, was analyzed using the high-throughput screening developed, and one variant, called M1′K2, was identified as having a distinctly improved specific activity. Therefore, this variant was produced in shaking flasks, and after purification via metal affinity chromatography, the improved specific activity was confirmed. In detail, the variant M1′K2 showed a more than twofold enhanced specific activity toward lactose at 8 °C compared to the parental β-galactosidase M1. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]