Comparison of cytogenetic and molecular features observed in endometrial cancers: known clinic and difficulties in treatment.

Objective: Understanding the relationship between genetic structure and the molecular changes involved in endometrial cancer (EC) provides an opportunity to personalize treatments and incorporate targeted therapies.

Method: We compared cytogenetic and molecular features observed in tumoral and adjacent healthy tissue endometrium samples in EC patients.

Results: Non-clonal chromosome aberrations (NCCAs) frequently in patients with EC, especially in 10,15,17,22, X chromosomes and were monitored in 73.

Understanding CO adsorption in MOFs combining atomic simulations and machine learning.

This study introduces a computational method integrating molecular simulations and machine learning (ML) to assess the CO adsorption capacities of synthesized and hypothetical metal-organic frameworks (MOFs) at various pressures. After extracting structural, chemical, and energy-based features of the synthesized and hypothetical MOFs (hMOFs), we conducted molecular simulations to compute CO adsorption in synthesized MOFs and used these simulation results to train ML models for predicting CO adsorption in hMOFs. Results showed that CO uptakes of synthesized MOFs and hMOFs are between 0.

Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications.

Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption.

Promoting longevity in aged liver through NLRP3 inflammasome inhibition using tauroursodeoxycholic acid (TUDCA) and SCD probiotics.

This investigation explores the combined influence of SCD Probiotics and tauroursodeoxycholic acid (TUDCA) on liver health in elderly male Sprague-Dawley rats. Through the administration of intravenous TUDCA (300 mg/kg) and oral SCD Probiotics (3 mL at 1 × 10^8 CFU) daily for one week, this study evaluates the biomolecular composition, histopathological alterations, and inflammasome activity in the liver. Analytical methods encompassed ATR-FTIR spectroscopy integrated with machine learning for the assessment of biomolecular structures, RT-qPCR for quantifying inflammasome markers (NLRP3, ASC, Caspase-1, IL18, IL1β), and histological examinations to assess liver pathology.

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics.

In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO/CH mixture separation under four different adsorption-based processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO/CH mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO/CH mixture adsorption and separation properties of all 68,614 COFs.

Supplementing probiotics during intermittent fasting proves more effective in restoring ileum and colon tissues in aged rats.

This study aimed to explore the impact of SCD Probiotics supplementation on biomolecule profiles and histopathology of ileum and colon tissues during a 30-day intermittent fasting (IF) program. Male Sprague-Dawley rats, aged 24 months, underwent 18-h daily fasting and received 3 mL (1 × 108 CFU) of SCD Probiotics. The differences in biomolecule profiles were determined using FTIR Spectroscopy and two machine learning techniques, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), which showed significant differences with high accuracy rates.

High-throughput computational screening of MOF adsorbents for efficient propane capture from air and natural gas mixtures.

In this study, we used a high-throughput computational screening approach to examine the potential of metal-organic frameworks (MOFs) for capturing propane (C3H8) from different gas mixtures. We focused on Quantum MOF (QMOF) database composed of both synthesized and hypothetical MOFs and performed Grand Canonical Monte Carlo (GCMC) simulations to compute C3H8/N2/O2/Ar and C3H8/C2H6/CH4 mixture adsorption properties of MOFs. The separation of C3H8 from air mixture and the simultaneous separation of C3H8 and C2H6 from CH4 were studied for six different adsorption-based processes at various temperatures and pressures, including vacuum-swing adsorption (VSA), pressure-swing adsorption (PSA), vacuum-temperature swing adsorption (VTSA), and pressure-temperature swing adsorption (PTSA).

Accelerated Discovery of Metal-Organic Frameworks for CO Capture by Artificial Intelligence.

The existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal-organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO adsorption and separation.

Hierarchical Computational Screening of Quantum Metal-Organic Framework Database to Identify Metal-Organic Frameworks for Volatile Organic-Compound Capture from Air.

The design and discovery of novel porous materials that can efficiently capture volatile organic compounds (VOCs) from air are critical to address one of the most important challenges of our world, air pollution. In this work, we studied a recently introduced metal-organic framework (MOF) database, namely, quantum MOF (QMOF) database, to unlock the potential of both experimentally synthesized and hypothetically generated structures for adsorption-based -butane (CH) capture from air. Configurational Bias Monte Carlo (CBMC) simulations were used to study the adsorption of a quaternary gas mixture of N, O, Ar, and CH in QMOFs for two different processes, pressure swing adsorption (PSA) and vacuum-swing adsorption (VSA).

Evaluating CH/N Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning.

Considering the large abundance and diversity of metal-organic frameworks (MOFs), evaluating the gas adsorption and separation performance of the entire MOF material space using solely experimental techniques or brute-force computer simulations is impractical. In this study, we integrated high-throughput molecular simulations with machine learning (ML) to explore the potential of both synthesized, the real MOFs, and computer-generated, the hypothetical MOFs (hypoMOFs), for adsorption-based CH/N separation. CH/N mixture adsorption data obtained from molecular simulations were used to train the ML models that could accurately predict gas uptakes of 4612 real MOFs.

SCD Probiotics mitigate cafeteria diet-induced liver damage in Wistar rats during development.

Background And Aim: The liver plays a critical role in metabolic homeostasis, and its health is often compromised by poor dietary habits. This study aimed to investigate the therapeutic potential of SCD Probiotics in mitigating adverse liver effects induced by a cafeteria diet in male Wistar rats during their developmental period.

Methods: Four groups of seven male Wistar rats each were subjected to different dietary regimens from day 21 (weaning) to day 56.

Reduced liver damage and fibrosis with combined SCD Probiotics and intermittent fasting in aged rat.

This study aimed to examine the impact of SCD Probiotics supplementation on liver biomolecule content and histological changes during a 30-day intermittent fasting (IF) program in 24-month-old male Sprague-Dawley rats. Rats underwent 18-h daily fasting and received 1 × 10 CFU of SCD Probiotics daily. Liver tissue biomolecules were analysed using FTIR Spectroscopy, LDA, and SVM techniques, while histopathological evaluations used Haematoxylin and eosin and Masson trichrome-stained tissues.

The rejuvenating influence of young plasma on aged intestine.

This study aims to investigate the effects of plasma exchange on the biomolecular profiles and histology of ileum and colon tissues in young and aged Sprague-Dawley male rats. Fourier transform infrared (FTIR) spectroscopy, linear discriminant analysis and support vector machine (SVM) techniques were employed to analyse the lipid, protein, and nucleic acid indices in young and aged rats. Following the application of young plasma, aged rats demonstrated biomolecular profiles similar to those of their younger counterparts.

Advancing CH/H separation with covalent organic frameworks by combining molecular simulations and machine learning.

A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH/H separation. We studied 597 synthesized COFs for adsorption of a CH/H mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH/H selectivities, CH working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified.

Composite of MIL-101(Cr) with a Pyrrolidinium-Based Ionic Liquid Providing High CO Selectivity.

Capturing CO selectively from flue gas and natural gas addresses the criteria of a sustainable society. In this work, we incorporated an ionic liquid (IL) (1-methyl-1-propyl pyrrolidinium dicyanamide, [MPPyr][DCA]) into a metal organic framework (MOF), MIL-101(Cr), by wet impregnation and characterized the resulting [MPPyr][DCA]/MIL-101(Cr) composite in deep detail to identify the interactions between [MPPyr][DCA] molecules and MIL-101(Cr). Consequences of these interactions on the CO/N, CO/CH, and CH/N separation performance of the composite were examined by volumetric gas adsorption measurements complemented by the density functional theory (DFT) calculations.

Age-related differences in response to plasma exchange in male rat liver tissues: insights from histopathological and machine-learning assisted spectrochemical analyses.

This study aimed to examine the biological effects of blood plasma exchange in liver tissues of aged and young rats using machine learning methods and spectrochemical and histopathological approaches. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were the machine learning algorithms employed. Young plasma was given to old male rats (24 months), while old plasma was given to young male rats (5 weeks) for thirty days.

Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF]/MOF Composites for CO/N Separation.

Considering the existence of a large number and variety of metal-organic frameworks (MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF composites by purely experimental methods is not practical. In this work, we combined molecular simulations and machine learning (ML) algorithms to computationally design an IL/MOF composite. Molecular simulations were first performed to screen approximately 1000 different composites of 1--butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF]) with a large variety of MOFs for CO and N adsorption.

Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques.

The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was obtained. The clinical decision support system for the diagnosis of GD was developed using the generated dataset with deep learning algorithms and Bayesian optimization.

Computational Investigation of Dual Filler-Incorporated Polymer Membranes for Efficient CO and H Separation: MOF/COF/Polymer Mixed Matrix Membranes.

Mixed matrix membranes (MMMs) composed of two different fillers such as metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) embedded into polymers provide enhanced gas separation performance. Since it is not possible to experimentally consider all possible combinations of MOFs, COFs, and polymers, developing computational methods is urgent to identify the best performing MOF-COF pairs to be used as dual fillers in polymer membranes for target gas separations. With this motivation, we combined molecular simulations of gas adsorption and diffusion in MOFs and COFs with theoretical permeation models to calculate H, N, CH, and CO permeabilities of almost a million types of MOF/COF/polymer MMMs.

Le Fort Partial Colpocleisis: An Early and Feasible Option in Pelvic Organ Prolapse.

Objective: To evaluate the safety and effectiveness of Le Fort Partial Colpocleisis (LFPC) in the surgical treatment of pelvic organ prolapse (POP) and to determine the incidence of pop recurrence in postoperative follow-up.

Study Design: Cross-sectional study.

Place And Duration Of Study: Ordu University Medical Faculty Training and Research Hospital, Ordu, Turkey, from June 2013 to November 2020.

An Evaluation of Cesarean Delivery Rates According to Robson Classification in the Black Sea Region of Turkey.

Objective: To analyse cesarean deliveries (CD) using the Ten Group Classification System (TGCS) for reducing cesarean rates.

Study Design: Cross-sectional study.

Place And Duration Of Study: Ordu University Medical Faculty Training and Research Hospital, Ordu, Turkey, from 1st January 2008 to 31st December 2020.

Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF/CH, CH/H, CH/N, and N/H mixtures.

The ease of functionalization of metal-organic frameworks (MOFs) can unlock unprecedented opportunities for gas adsorption and separation applications as the functional groups can impart favorable/unfavorable regions/interactions for the desired/undesired adsorbates. In this study, the effects of the presence of multiple functional groups in MOFs on their CF/CH, CH/H, CH/N, and N/H separation performances were computationally investigated combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The most promising adsorbents showing the best combinations of selectivity, working capacity, and regenerability were identified for each gas separation.