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Exploring the structural basis of crystals that affect nonlinear optical responses: An experimental and machine learning quest
(Elsevier B.V., 2025) Hassan, Abrar U.; Güleryüz, Cihat; El Azab, Islam H.; Elnaggar, Ashraf Y.; Mahmoud, Mohamed H.H.
Machine learning can enable a computational framework to learn from data, thereby enhancing decision-making for targeted properties. Based on the significance of nonconjugated crystals as effective switches, an ML based approach has been applied to evaluate driving forces behind their polarizability/hyperpolarizability related hyper-Rayleigh Scattering (βHRS). For this, a dataset of relevant 1,3,5-triazine-2,4,6-triamine related structures in collected from peer reviewed literature to design its molecular descriptors. The designed dataset is trained on different regression models along with their cross-validation techniques include K-Fold and Leave One Group Out. It shows that Random Forest Regression can predict their polarizabilities with a fair accuracy (R2 = 0.83). Additionally, it shows its energy gaps (Egaps) ranging from 4.62 to 4.89 eV, with the smallest gap observed in ethanol. Understanding both these theoretical and experimental calculations can significantly help in selecting materials for targeted purposes, including sensors, electronic devices, and catalysis. Furthermore, insights into nucleophilic tendencies and charge distributions aids in designing new materials with tailored properties, expanding their use in various applications across chemistry, materials science, and other fields. The ML techniques prove its effectiveness to predict polarizabilities in response to its computational realm due to feature design, regression models with their cross-validations.
A fast and efficient machine learning assisted prediction of urea and its derivatives to screen crystal propensity with experimental validation
(Elsevier Ltd, 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Noreen, Sadaf; Elnaggar, Ashraf Y.
Predicting crystal propensity is crucial yet challenging in various industries where it significantly influences product stability, performance, and efficacy. Predicting a crystal propensity identifies their optimal chemical structures for desired properties including solubility, bioavailability, shelf-life stability etc. Herein, A machine learning (ML) assisted analysis is performed to predict their crystal propensity by collecting a dataset of 6000 non-crystalline and over 200 crystalline urea and its derivatives. The data is trained by employing a Support Vector Machine (SVM) with its Radial Basis Function (RBF) and linear kernels along with Random Forest regression analysis. The trained data is compared with four other ML models, including Linear Regression, Gradient Boosting, Random Forest and Decision Tree Regressions to predict their crystal propensity. It yields an accuracy of 79 % for identifying their non-crystalline compounds and 59 % in predicting crystallization failure. Their dimensionality reduction via t-SNE reveals their distinct clustering patterns to underscore their complex interplay between molecular structure and crystal propensity. Their experimental validation also corroborates the current findings to demonstrate their efficacy to streamline their crystal engineering for pharmaceutical formulation-based workflows. Notably, the number of rotatable bonds and molecular connectivity index (χov) emerges as pivotal descriptors for enabling their accurate classification with minimal input features. This study elucidates its quantitative structure-crystallinity relationship to provide a valuable tool for crystal design and optimization.
Benzothiophene semiconductor polymer design by machine learning with low exciton binding energy: A vast chemical space generation for new structures
(Elsevier Ltd, 2025) Mallah, Shaimaa H.; Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Güleryüz, Hasan; Mohyuddin, Ayesha; Kyhoiesh, Hussein A.K.
The development of new organic semiconductors with low exciton binding energies (Eb) is crucial for improving the efficiency of organic photovoltaic (PV) devices. Here, we report the generation of a chemical space of benzothiophene (BDT)-based organic semiconductors with lowest Eb energies using machine learning (ML). Our study involves the design of over 500 organic semiconductor structures with low Eb energies and their synthetic accessibility scores. For this, we collect 1061 BDT based compounds from literature, calculated their Eb energies, and predicted them using ML with Random Forest (RF) regression, yielding the best results. Our analysis, using SHAP values, reveals that heavy atoms are the main factors in lowering Eb values. Furthermore, we tested new organic chromophore structures, which showed an efficient shift of their molecular charges. The UV–Vis spectra of these structures exhibits a redshift in the range of 358–667 nm, while their open-circuit voltage (Voc) and light-harvesting efficiency (LHE) ranges from 1.64 to 1.954 V and 52–91 %, respectively. Current study provides a valuable chemical space for the development of new organic semiconductors with improved efficiency. © 2025 Elsevier Ltd
A machine learning and data-oriented quest to screen the degree of long-range order/disorder in polymeric materials
(Elsevier, 2025) Guleryuz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Mahmoud, Mohamed. H. H.
In the realm of polymeric materials, the delicate balance between long-range order and disorder dictates crystal properties, influencing their performance in various applications. To unravel this enigma, we embarked on a machine learning (ML) and data-driven quest, compiling 2500 data points from literature. By harnessing the power of Support Vector Machines (SVM) and Radial Basis Functions (RBF), we trained our model to decipher the intricate relationships between molecular descriptors and crystal properties. Introducing a novel pass/fail system, we screened polymers based on their calculated descriptors, revealing that combining multiple descriptors significantly enhances model performance. Identifying 1200 polymers that failed to meet crystallization requirements provides valuable insights for designing materials with tailored structural features. This groundbreaking study pioneers a data-oriented approach to understanding polymeric materials, paving the way for the creation of novel crystals with optimized properties. By uncovering the hidden patterns of order and disorder, we unlock the secrets of polymeric materials, revolutionizing their applications in various fields.
One step synthesis of tryptophan-isatin carbon nano dots and bio-applications as multifunctional nanoplatforms
(2025) Tok, Kerem; Barlas, F. Barış; Bayır, Ece; Şenışık, Ahmet Murat; Zihnioğlu, Figen; Timur, Suna
The development of natural molecule-derived carbon nano dots (CNDs) marks a significant advancement in biocompatible and sustainable nanomaterials. Tryptophan, capable of crossing the blood-brain barrier (BBB), serves as a precursor to numerous pharmacologically active compounds, while isatin and its derivatives have demonstrated anti-tumor effects, including against brain cancers. This study aimed to synthesize fluorescent CNDs from tryptophan-isatin hybrid precursor and explore their applications in glioblastoma treatment. These CNDs were characterized using techniques such as TEM, SEM-EDS, FTIR, XPS, Raman spectroscopy and UV-Vis spectrophotometry. In vitro tests using the U-87 glioblastoma cell line evaluated cell viability, affinity, and BBB permeability. The CNDs, between 4 and 7 nm in size, exhibited blue and green fluorescence, with no cytotoxic effects observed at concentrations up to 25 µg/mL. The highest BBB permeability rate was determined as 4.3 × 10⁻⁵ cm/s. Additionally, the CNDs demonstrated radiotherapeutic properties, leading to a 51 % reduction in cell viability. This research contributes to nanomedicine by introducing a novel biocompatible material with potential for targeted brain cancer imaging and therapy, while also suggesting broader applications beyond glioblastoma.
On the quest for solar energy harvesters and nonlinear optics: a DFT exploration of A-D-D-A framework with varying sp2 hybridization
(Springer, 2025) Güleryüz, Cihat; Rehman, Muhammad M. U.; Hassan, Abrar U.; Abass, Zainab A.; Mohyuddin, Ayesha; Zafar, Muddassar; Alotaibi, Mohammed T.
In response to address the constraints of fullerene analogues, scientists are constantly working on developing low-cost fullerene-free functionalization for nanoscale organic photovoltaics. During the present study, the computational design and analysis of 14 new non-fullerene dyes (IDIC-O-1 to IDIC-O-14) centered on indacenodithiophene (IDIC) core are proposed with sp2-hybridized nitrogen at varying positions. Regarding their UV–visible assessment, several long-range and range-separated functionals like B3LYP, CAM-B3LYP, ωB97XD, and APFD using the 6-311G + (d,p) basis set have been employed to identify their optimal level of density functional theory (DFT) with an impressive correlation at the CAM-B3LYP level. Their global hardness (η) and global electrophilicity (ω) natures show their persistent nature. The energy gaps (Egaps) are lesser than IDIC and IDIC-O to imply an easier electronic transition. When contrasted to the IDIC-O, the findings indicate that its broad absorption spectrum had a redshift. The efficient HOMO → LUMO-based CT was investigated, and an open-circuit voltage (Voc) study is done on HOMOIDIC → LUMOAcceptor. All dyes have their Voc values lower than reference (IDIC-O) except IDIC-O-11 with a positive value. These lower reorganization energies (RE) for holes and electrons indicate a greater charge transfer (CT). When contrasted to the IDIC-O, the newly designed dyes have better characteristics for solar cell performance.
Exploring structural basis of photovoltaic dye materials to tune power conversion efficiencies: A DFT and ML analysis of Violanthrone
(Elsevier Ltd, 2025) Sumrra, Sajjad H.; Güleryüz, Cihat; Hassan, Abrar U.; Abass, Zainab A.; Hanoon, Talib M.; Mohyuddin, Ayesha; Kyhoiesh, Hussein A.K.; Alotaibi, Mohammed T.
This study employs a systematic approach to modify Violanthrone (V) structures and analyze their impact on photovoltaic (PV) properties. We use cheminformatics based Python library based RDKit tool to calculate their structural descriptors for to correlate them with their PV parameters. Our analysis reveals a positive correlation for their Open-Circuit Voltage (Voc) and Fill Factor (FF) for indicating that their higher voltage output is associated for their efficient charge carrier mobilities. We also predict their Power Conversion Efficiency (PCE) by drawing their their Scharber diagram which achieves their promising efficiency of up to 15 %. To further enhance the reliability our work, we conduct an extensive literature survey of such organic materials to predict their PCEs by their Machine Learning (ML) after utilizing various ML models. Among five tested ML models, it identifies the Random Forecast (RF) model and Gradient Boosting (GB) models as as the optimal one (R-squared value: 0.82). Their feature importance reveals that their FF is the most significant feature to impact their PCEs (importance value: 10.9). Furthermore, we observe a negative correlation between orbital interaction strength (E(2)) values and orbital energy differences E(j)-E(i) which indicates that their stronger orbital interactions are associated with their smaller energy differences. Our study provides valuable insights for their structural basis to PV material designs for enabling their design for efficient materials in energy conversion.
A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications
(Elsevier B.V., 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Waheeb, Azal S.; Awad, Masar A.; Jalfan, Ayad R.; Noreen, Sadaf; Kyhoiesh, Hussein A.K.; El Azab, Islam H.
We present a synergistic approach to combine Machine Learning (ML), Density Functional Theory (DFT), and molecular descriptor analysis for designing high-performance benzodithiophene (BDT) based chromophores. A dataset of 366 BDT incorporated moieties is compiled from literature while their molecular descriptors are designed by using Python programming language. Linear and Random Forest Regression models produces best results to predict their exciton binding energy (Eb) with their R-Squared (R2) value 0.87 and 0.94 respectively. Their DFT calculations provides additional features, including molecular charges. Their ML models also reveals that their Eb values are a crucial predictor for their photovoltaic (PV) performance as its lower value could facilitate efficient charge carrier separation. For this, their hydrogen bond acceptors (HBA) and topological polar surface area (TPSA) emerges as key descriptors during their regression analysis. Their DFT validation shows negligible differences in their molecular charges to suggest their electron donor/acceptor moieties can significantly impact their chromophore nature. The current research work is helpful for efficiently screening the suitability of organic chromophores for their PV applications through advanced computational tools.
Evaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials : A DFT and ML analysis
(Elsevier Ltd, 2024) Kadhum, Afaf M.; Waheeb, Azal S.; Awad, Masar A.; Hassan, Abrar U.; Sumrra, Sajjad H.; Güleryüz, Cihat; Mohyuddin, Ayesha; Noreen, Sadaf; Kyhoiesh, Hussein A.K.; Alotaibi, Mohammed T.
We present a new study on the design, discovery and space generation of carbon selenide based photovoltaic (PV) materials. By extending acceptors and leveraging density functional theory (DFT) and machine learning (ML) analysis, we discover new QDs with remarkable PV properties. We employ various ML models, to correlate the exciton binding energy (Eb) of 938 relevant compounds from literature with their molecular descriptors of structural features that influence their performance. Our study demonstrates the potential of ML approaches in streamlining the design and discovery of high-efficiency PV materials. Also the RDKit computed molecular descriptors correlates with PV parameters revealed maximum absorption (λmax) ranges of 509–531 nm, light harvesting efficiency (LHE) above 92 %, Open Circuit Voltage (Voc) of 0.22–0.45 V, and short Circuit (Jsc) currents of 37.92–42.75 mA/cm2. Their Predicted Power Conversion Efficiencies (PCE) using the Scharber method reaches upto 09–13 %. This study can pave the way for molecular descriptor-based design of new PV materials, promising a paradigm shift in the development of high-efficiency solar energy conversion technologies.
Molecular engineering on tyrian puprle natural dye as TiO2 based fined tuned photovoltaic dye material: DFT molecular analysis
(2024) Güleryüz, Cihat; Hasan, Duha M.; Awad, Masar A.; Waheeb, Azal S.; Hassan, Abarar U.; Mohyuddin, Ayesha; Kyhoiesh, Hussein A. K.; Alotaibi, Mohammed T.
In this research, molecular modification is employed to see the enhancement in the efficiency of Tyrian Purple (TP), a natural dye, for organic photovoltaic materials. By using Density Functional Theory (DFT) based molecular modeling, seven new structures are designed with pi spacer to extend electron donor moieties. Teheir Frontier Molecular Orbital (FMO) analysis demonstartes their charges with a similar pattern of distributions over their Highest Occupied and Lowed Unocuupied Molecular Orbitals (HOMO/lUMO). This analysls also show their energy gaps (Egaps) to range around 2.97-3.02 eV. Their maximum absorption wavelength (λmax) demosntartes 486-490 nm range to indicate their tendency of absorbing light efficiently. Their Transition Density Matrix (TDM) analysis also reveals their facile electronic transitions without a significant charges over spacers. From calculating their photovoltaic paramters, their Light Harvesting Efficiency (LHE) reaches to 72.4-95.5 %. Also their Open Circuit Voltage (Voc) varies across 1.16-1.34 V. It is found that dyes actively adsorb onto TiO2 clusters to demonstrate their promise for tuning their Conduction Band (CB). This research is an effort for to evaluate the structural correlations to the develop photovoltaic materials through molecular-level design and optimization.
Theoretical calculations of nonlinear optical responses for interpreting nonconjugated molecular systems to affect non-optimal properties
(2024) Güleryüz, Cihat; Sumrra, Sajjad H.; Mohyuddin, Ayesha; Hassan, Abrar U.; Dahshan, Alaa
Understanding the intricacies of polymorphic origins in nonconjugated crystal systems is crucial for optimizing their properties. This study focuses on the crystal growth, characterization, and nonlinear optical (NLO) responses of a system analyzed using single crystal X-ray analysis, revealing a monoclinic geometry. Hirschfeld surface analysis emphasized the significance of intermolecular interactions in driving polymorph development, shedding light on the structural nuances influencing the material's properties. Through the evaluation of density functional theory parameters, the research found that the NLO responses of the system were as efficient as those of widely recognized materials like urea and KDP. Moreover, the stability of the system was confirmed through (NBO) analysis, showcasing its potential for practical applications. By manipulating the polymorphic crystal forms, researchers can potentially unveil new crystalline materials with tailored properties suitable for applications in optical and optoelectronic devices. This work underscores the importance of exploring novel crystal engineering strategies to harness the full potential of materials in the realm of advanced technologies.
Transfer and persistence of microbiota markers from the human hand to the knife: A preliminary study
(2024) Karadayı, Şükriye; Yılmaz, İlknur; Özbek, Tülin; Karadayı, Beytullah
New scientific techniques and methods are always needed to link the perpetrators to the incident or the crime scene. Recent microbiota studies based on NGS (Next-generation sequencing) show that various biological samples from crime scenes have the potential to be used in forensic investigations. Especially when DNA traces belonging to more than one person are insufficient to fully determine the genetic profile, a secret sample, such as a microbiota sample created by the suspect's touch, can be used. In this preliminary study, a fictionalized experimental model was designed to investigate the transfer and persistence of the hand microbiome on the knife handle, which has a high potential to be used in criminal incidents, by metagenomic analysis methods. In addition, it was aimed to determine the transfer of specific bacterial species identified only to the person among the five participants onto the knife handle and their persistence over time. In the first stage of the research, samples were collected from the hands of 5 volunteer participants using the swap method, including their palms. Then, after each participant held a different knife, samples were collected from the knife handles via swabs from different angles of the knives at 4 and 24 h and analyzed by metagenomic methods. The findings of this preliminary study showed that the heatmap graphs generated after UniFrac distance analysis were not successful in establishing any similarity between the hand samples and the post-transfer knife handle samples. Nonetheless, it was observed that the transfer of bacterial species detected in the hand samples to knives differed according to the individuals and some bacterial species were transferred to the knife samples held by the participants. The number of bacterial species detected that are specific to each participant's hand sample was 302 in total, and it was determined that a total of 8.28 % of these bacterial species were transferred to the knife handle samples of the 4th hour and 6.95 % to the knife samples of the 24th hour. In the presented study, considering the transfer of some bacterial species in the hand microbiome, which are effective in the variation between individuals, onto the knife; It has been evaluated that some rare bacterial species can be important potential markers to associate the object with the perpetrator.
Exploration of deep UV-NLO responses of non-conjugated crystal systems by harnessing aprotic solvents: DFT vs experimental
(2024) Güleryüz, Cihat; Mohyuddin, Ayesha; Hassan, Abrar U.
This study introduces a newly found form of a non-conjugated crystal called 1,3,5-triazine-2,4,6-triamine polymorph (TT), TT ), which demonstrates a well-rounded UV nonlinear optical (NLO) behavior. By employing solvent-assisted Tauc bandgap adjustment, the crystal's structure was altered, leading to improved optical characteristics. The material's monoclinic polymorph displays adjustable bandgaps that are impacted by the choice of solvent during the crystallization process. When evaluated against a chemical potential of-2.61 eV, its characteristics were forecasted. Charge displacement across the aromatic ring is observed in the solvent stage, while molecular electrostatic potential (MEP) surfaces highlighted the nitrogen atoms as nucleophilic. The comparative examination of its energy gaps (E gaps ) offers significant perspectives on their prospective uses. Understanding these energy differences is crucial for choosing materials for specific applications like electronic devices, sensors, or catalysis. Moreover, comprehending charge distributions and nucleophilic tendencies can influence the creation of new materials with customized attributes for diverse uses in fields such as chemistry, materials science, and beyond.
Innovative fluorescent polymers in niosomal carriers: a novel approach to enhancing cancer therapy and imaging
(2024) Tornacı, Selay; Erginer, Merve; Bulut, Umut; Şener, Beste; Persilioğlu, Elifsu; Kalaycılar, İsmail Bergutay; Çelik, Emine Güler; Yardibi, Hasret; Siyah, Pınar; Karakurt, Oğuzhan; Çırpan, Ali; Gökalsın, Barış; Şenışık, Ahmet Murat; Barlas, Fırat Barış
Cancer is anticipated to become the pioneer reason of disease-related deaths worldwide in the next two decades, underscoring the urgent need for personalized and adaptive treatment strategies. These strategies are crucial due to the high variability in drug efficacy and the tendency of cancer cells to develop resistance. This study investigates the potential of theranostic nanotechnology using three innovative fluorescent polymers (FP-1, FP-2, and FP-3) encapsulated in niosomal carriers, combining therapy (chemotherapy and radiotherapy) with fluorescence imaging. These cargoes are assessed for their cytotoxic effects across three cancer cell lines (A549, MCF-7, and HOb), with further analysis to determine their capacity to augment the effects of radiotherapy using a Linear Accelerator (LINAC) at specific doses. Fluorescence microscopy is utilized to verify their uptake and localization in cancerous versus healthy cell lines. The results confirmed that these niosomal cargoes not only improved the antiproliferative effects of radiotherapy but also demonstrate the practical application of fluorescent polymers in in vitro imaging. This dual function underscores the importance of dose optimization to maximize therapeutic benefits while minimizing adverse effects, thereby enhancing the overall efficacy of cancer treatments.
Investigation of neurosphere activity of injectable 3D graphene bioink biomaterial
(2024) Yıldız, Aslı Pınar Zorba; Yavuz, Burçak; Abamor, Emrah Şefik; Darıcı, Hakan; Allahverdiyev, Adil
PurposeThe aim of this study includes the comparative examination of neurosphere formation by WJ-derived mesenchymal stem cells in both 2D media and 3D injectable graphene and graphene-free bioink systems in terms of both immunostaining and gene expression levels.MethodsFor this purpose, hydrogel bioinks were first created and the wj-MKH spheroidal structure was formed on 3D-B (without graphene) and 3D-G (containing graphene). Then, following the differentiation procedure, neurosphere transformations were identified by both immunostaining (b-III Tubulin and Sox2), and Tubulin 3, Sox2, and Nestin markers were examined at the gene expression level with Real-Time PCR, and the results were compared with the 2D environment.ResultsAccording to the results obtained, neurosphere formation occurred more in the 3D environment compared to the 2D environment, obtained both by immunostaining and gene expression levels. It was also observed that differentiation formed neuron-like structures, especially in the 3D-G group containing graphene.ConclusionAs a result, it has been observed that the use of graphene with a non-toxic concentration in the hydrogel injectable system provides better differentiation of stem cells, especially those that will form the cell leg of the biomaterial.Lay SummaryTherefore, the use of graphene-containing hydrogels in injectable systems in nerve damage may increase the effectiveness on nerve regeneration.
Applications of stem cell-derived extracellular vesicles in nerve regeneration
(2024) Mutlu, Esra Cansever; Ahmed, Zubair; Ben-Nissan, Besim; Stamboulis, Artemis; Yavuz, Burçak
Extracellular vesicles (EVs), including exosomes, microvesicles, and other lipid vesicles derived from cells, play a pivotal role in intercellular communication by transferring information between cells. EVs secreted by progenitor and stem cells have been associated with the therapeutic effects observed in cell-based therapies, and they also contribute to tissue regeneration following injury, such as in orthopaedic surgery cases. This review explores the involvement of EVs in nerve regeneration, their potential as drug carriers, and their significance in stem cell research and cell-free therapies. It underscores the importance of bioengineers comprehending and manipulating EV activity to optimize the efficacy of tissue engineering and regenerative therapies.
Detection methods for Legionella pneumophila in diverse environmental conditions: A comparative study of FISH, seminested PCR and conventional culture
(2024) Alver, İpek Ada; Kimiran, Ayten
Aims: Legionella bacteria cause Legionnaires' disease and Pontiac fever. It is commonly found in natural water resources and manmade water systems. Environmental conditions such as nutrient deficiency, temperature, pH, disinfectant and the presence of other bacteria can cause Legionella bacteria to pass into the viable but not-culturable (VBNC) phase. This study was aimed to determine appropriate methods to detect Legionella pneumophila bacteria living in water systems with wide temperature and pH ranges threatening human health. Methodology and results: In this study, water samples containing L. pneumophila at a concentration of 108 cell/L were exposed to different temperatures (5 degrees C, 50 degrees C, 55 degrees C and 60 degrees C) and pH (2.2, 5.8, 7.0 and 8.2) values. Conventional culture, FISH and seminested PCR methods were used to detect L. pneumophila. A comparison was made between the methods used in the study to determine the most appropriate method for detecting L. pneumophila bacteria. The results showed that the highest detection rates of L. pneumophila were at 5 degrees C for 24 h (100%) and at pH 2.2 for 0th min (100%) by using FISH method. All the samples could be determined by the seminested PCR method. The results of our study showed that the highest detection rates of L. pneumophila were at 5 degrees C for 24 h (100%) and at pH 2.2 for 0 min (100%) by FISH method. All of the samples could be determined by the seminested PCR method. It was determined that the detection rate was the lowest in the FISH method at 3 min at 60 degrees C and the highest was 24 h at 5 degrees C. The lowest detection rate was also observed by using FISH method in the samples exposed to 60 degrees C for 3 min. Results show that the FISH and seminested PCR methods are the most suitable for detecting L. pneumophila bacteria from water systems exposed to different environmental conditions. Conclusion, significance and impact of study: Different methods (conventional culture, FISH, seminested PCR) used to detect L. pneumophila bacteria were compared in this study. It was concluded that Legionella bacteria passed into the VBNC phase, and compared to molecular methods, the conventional culture method provides a low detection rate of these bacteria. Research findings suggest that it is insufficient to use the conventional culture method alone for the detection of Legionella bacteria from man-made water systems or human samples. This study is important as it is decisive for the determination of the most appropriate method for detecting the human pathogen L. pneumophila bacteria from water samples and the choice for a fast and effective method for the elimination of the infectious agent.
Evaluation of Bacteriophage ?11 host recognition protein and its host-binding peptides for diagnosing/targeting of Saphylococcus aureus infections
(2024) Dokuz, Senanur; Taşdurmazlı, Semra; Acar, Tayfun; Duran, Gizem Nur; Özdemir, Çilem; Özbey, Utku; Özbil, Mehmet; Karadayı, Şükriye; Bayrak, Ömer Faruk; Derman, Serap; Chen, John Yu-Shen; Özbek, Tülin
Evaluating the potential of using both synthetic and biological products as targeting agents for the diagnosis, imaging, and treatment of infections due to particularly antibiotic-resistant pathogens is important for controlling infections. We examined the interaction between Gp45, a receptor-binding protein of the ϕ11 lysogenic phage, and its host S. aureus, a common cause of nosocomial infections. Using molecular dynamics and docking simulations, we identified the peptides that bind to S. aureus wall teichoic acids via Gp45. We compared the binding affinity of Gp45 and the two highest-scoring peptide sequences (P1 and P3) and their scrambled forms using microscopy, spectroscopy, and ELISA. Our results revealed that rGp45 (recombinant Gp45) and chemically synthesized P1 had a higher binding affinity for S. aureus compared with all other peptides, with the exception of E. coli. Furthermore, rGp45 had a capture efficiency of over 86%; P1 had a capture efficiency of over 64%. Overall, our findings suggest that receptor-binding proteins such as rGp45, which provide a critical initiation of the phage life cycle for host adsorption, might play an important role in the diagnosis, imaging, and targeting of bacterial infections. Studying such proteins could accordingly enable the development of effective strategies for controlling infections.
Lipases for targeted industrial applications, focusing on the development of biotechnologically significant aspects: A comprehensive review of recent trends in protein engineering
(2024) Vardar-Yel, Nurcan; Tütüncü, Havva Esra; Sürmeli, Yusuf
Lipases are remarkable biocatalysts, adept at catalyzing the breakdown of diverse compounds into glycerol, fatty acids, and mono- and di-glycerides via hydrolysis. Beyond this, they facilitate esterification, transesterification, alcoholysis, acidolysis, and more, making them versatile in industrial applications. In industrial processes, lipases that exhibit high stability are favored as they can withstand harsh conditions. However, most native lipases are unable to endure adverse conditions, making them unsuitable for industrial use. Protein engineering proves to be a potent technology in the development of lipases that can function effectively under challenging conditions and fulfill criteria for various industrial processes. This review concentrated on new trends in protein engineering to enhance the diversity of lipase genes and employed in silico methods for predicting and comprehensively analyzing target mutations in lipases. Additionally, key molecular factors associated with industrial characteristics of lipases, including thermostability, solvent tolerance, catalytic activity, and substrate preference have been elucidated. The present review delved into how industrial traits can be enhanced through directed evolution (epPCR, gene shuffling), rational design (FRESCO, ASR), combined engineering strategies (i.e. CAST, ISM, and FRISM) as protein engineering methodologies in contexts of biodiesel production, food processing, and applications of detergent, pharmaceutics, and plastic degradation.
Periodontal disease - a late complication of head and neck cancer radiotherapy
(2024) Brandt, Ella; Keskin, Mutlu; Raisanen, Ismo T.; Makitie, Antti; Patila, Tommi; Sorsa, Timo; Gupta, Shipra
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