Therapeutic mammoplasty: a “wise” oncoplastic choice—lessons from the largest single-center cohort from Asia

IntroductionThe majority of breast cancer patients from India usually present with advanced disease, limiting the scope of breast conservation surgery. Therapeutic mammoplasty (TM), an oncoplastic technique that permits larger excisions, is quite promising in such a scenario and well suited to breast cancer in medium-to-large-sized breasts with ptosis and in some cases of large or multifocal/multicentric tumors. Here, we describe our TM cohort of 205 (194 malignant and 11 benign) patients from 2012 to 2019 treated at a single surgeon center in India, the largest Asian dataset for TM.MethodsAll patients underwent treatment after careful discussions by a multidisciplinary tumor board and patient counseling. We report the clinicopathological profiles and surgical, oncological, cosmetic, and patient-related outcomes with different TM procedures.ResultsThe median age of breast cancer patients was 49 years; that of benign disease patients was 41 years. The breast cancer cohort underwent simple (n = 84), complex (n = 71), or extreme (n = 44) TM surgeries. All resection margins were analyzed through intra-operative frozen-section assessment with stringent rad-path analysis protocols. The margin positivity rate was found to be 1.4%. A majority of the cohort was observed to have pT1–pT2 tumors, and the median resection volume was 180 cc. Low post-operative complication rates and good-to-excellent cosmetic scores were observed. The median follow-up was 39 months. We observed 2.07% local and 5.7% distal recurrences, and disease-specific mortality was 3.1%. At median follow-up, the overall survival was observed to be 95.9%, and disease-free survival was found to be 92.2%. The patient-reported outcome measures (PROMs) showed good-to-excellent scores for all types of TMs across BREAST-Q domains.ConclusionWe conclude that in India, a country where women present with large and locally advanced tumors, TM safely expands the indications for breast conservation surgery. Our results show oncological and cosmetic outcomes at acceptable levels. Most importantly, PROM scores suggest improved overall wellbeing and better satisfaction with the quality of life. For patients with macromastia, this technique not only focuses on cancer but also improves self-image and reduces associated physical discomfort often overlooked by women in the Indian setting. The popularization of this procedure will enable Indian patients with breast cancer to receive the benefits of breast conservation

Development of New Transferable Coarse-Grained Models of Hydrocarbons

We have utilized an approach that integrates molecular dynamics (MD) simulations with particle swarm optimization (PSO) to accelerate the development of coarse-grained (CG) models of hydrocarbons. Specifically, we have developed new transferable CG beads, which can be used to model the hydrocarbons (C5 to C17) and reproduce their experimental properties with good accuracy. First, the PSO method was used to develop the CG beads of the decane model represented with a 2:1 (2-2-2-2-2) mapping scheme. This was followed by the development of the nonane model described with hybrid 2-2-3-2 and 3:1 (3-3-3) mapping schemes. The force-field parameters for these three CG models were optimized to reproduce four experimentally observed properties including density, enthalpy of vaporization, surface tension, and self-diffusion coefficient at 300 K. The CG MD simulations conducted with these new CG models of decane and nonane, at different timesteps, for various system sizes, and at a range of different temperatures, were able to predict their density, enthalpy of vaporization, surface tension, self-diffusion coefficient, expansibility, and isothermal compressibility with good accuracy. Moreover, a comparison of structural features obtained from the CG MD simulations and the CG beads of mapped all-atom trajectories of decane and nonane showed very good agreement. To test the chemical transferability of these models, we have constructed the models for hydrocarbons ranging from pentane to heptadecane, by using different combinations of the CG beads of decane and nonane. The properties of pentane to heptadecane predicted by these new CG models showed excellent agreement with the experimental data

Development of New Transferable Coarse-Grained Models of Hydrocarbons

We have utilized an approach that integrates molecular dynamics (MD) simulations with particle swarm optimization (PSO) to accelerate the development of coarse-grained (CG) models of hydrocarbons. Specifically, we have developed new transferable CG beads, which can be used to model the hydrocarbons (C5 to C17) and reproduce their experimental properties with good accuracy. First, the PSO method was used to develop the CG beads of the decane model represented with a 2:1 (2-2-2-2-2) mapping scheme. This was followed by the development of the nonane model described with hybrid 2-2-3-2 and 3:1 (3-3-3) mapping schemes. The force-field parameters for these three CG models were optimized to reproduce four experimentally observed properties including density, enthalpy of vaporization, surface tension, and self-diffusion coefficient at 300 K. The CG MD simulations conducted with these new CG models of decane and nonane, at different timesteps, for various system sizes, and at a range of different temperatures, were able to predict their density, enthalpy of vaporization, surface tension, self-diffusion coefficient, expansibility, and isothermal compressibility with good accuracy. Moreover, a comparison of structural features obtained from the CG MD simulations and the CG beads of mapped all-atom trajectories of decane and nonane showed very good agreement. To test the chemical transferability of these models, we have constructed the models for hydrocarbons ranging from pentane to heptadecane, by using different combinations of the CG beads of decane and nonane. The properties of pentane to heptadecane predicted by these new CG models showed excellent agreement with the experimental data

Development of New Transferable Coarse-Grained Models of Hydrocarbons

We have utilized an approach that integrates molecular dynamics (MD) simulations with particle swarm optimization (PSO) to accelerate the development of coarse-grained (CG) models of hydrocarbons. Specifically, we have developed new transferable CG beads, which can be used to model the hydrocarbons (C5 to C17) and reproduce their experimental properties with good accuracy. First, the PSO method was used to develop the CG beads of the decane model represented with a 2:1 (2-2-2-2-2) mapping scheme. This was followed by the development of the nonane model described with hybrid 2-2-3-2 and 3:1 (3-3-3) mapping schemes. The force-field parameters for these three CG models were optimized to reproduce four experimentally observed properties including density, enthalpy of vaporization, surface tension, and self-diffusion coefficient at 300 K. The CG MD simulations conducted with these new CG models of decane and nonane, at different timesteps, for various system sizes, and at a range of different temperatures, were able to predict their density, enthalpy of vaporization, surface tension, self-diffusion coefficient, expansibility, and isothermal compressibility with good accuracy. Moreover, a comparison of structural features obtained from the CG MD simulations and the CG beads of mapped all-atom trajectories of decane and nonane showed very good agreement. To test the chemical transferability of these models, we have constructed the models for hydrocarbons ranging from pentane to heptadecane, by using different combinations of the CG beads of decane and nonane. The properties of pentane to heptadecane predicted by these new CG models showed excellent agreement with the experimental data

A Comparison between the Lower Critical Solution Temperature Behavior of Polymers and Biomacromolecules

All-atom molecular dynamics (MD) simulations are employed to compare the lower critical solution temperature (LCST) behaviors of poly(N-isopropylacrylamide) (PNIPAM) and elastin-like polypeptides (ELPs) with the canonical Val-Pro-Gly-Val-Gly ((VPGVG)n) sequence over a range of temperatures from 280 K to 380 K. Our simulations suggest that the structure of proximal water dictates the conformation of both the (VPGVG)n ELPs and PNIPAM chains. Specifically, the LCST transition in ELPs can be attributed to a combination of thermal disruption of the network of the proximal water near both hydrophilic and hydrophobic groups in the backbone and side-chain of (VPGVG)n, resulting in a reduction in solvent accessible surface area (SASA). This is accompanied with an increase in the secondary structure above its LCST. In the case of PNIPAM, the LCST transition is a result of a combination of a reduction in the hydrophobic SASA primarily due to the contributions of isopropyl side-chain and less to the backbone and the formation of intra-chain hydrogen bonds between the amide groups on the side-chain above its LCST

Machine-Learned Coarse-Grained Models

Optimizing force-field (FF) parameters to perform molecular dynamics (MD) simulations is a challenging and time-consuming process. We present a novel FF optimization framework that integrates MD simulations with particle swarm optimization (PSO) algorithm and artificial neural network (ANN). This new ANN-assisted PSO framework was used to develop transferable coarse-grained (CG) models for D₂O and DMF as a proof of concept. The PSO algorithm was used to generate the set of input FF parameters for the MD simulations of the CG models of these solvents, which were optimized to reproduce their experimental properties. Herein, for the first time, a reverse approach was employed for on-the-fly training of the ANN model, where results (solvent properties) obtained from the MD simulations and their corresponding FF parameters were used as inputs and outputs, respectively. The ANN model was then required to predict a set of new FF parameters, which were tested for their ability to predict the desired experimental properties. This new framework can be extended to integrate any optimization algorithm with ANN and MD simulations to accelerate the FF development

Formulation And Evaluation Of Fast Dis-integrating Tablets Of Sertraline Hcl By Using Natural Super Disintegrants

The speed of onset of action of antidepressant is clinically important for several reasons. Objective of this research was to formulate Fast disintegrating tablets of sertraline hydrochloride by using natural superdisintegrants so that it will minimize time of onset of action and also become economic. Direct compression method is use to prepare Fast disintegrating tablets. Paper describes impact of different concentration of natural superdisintegrants like gelatinized starch, treated agar on various parameters of Fast disintegrating tablets of sertraline Hcl. All formulation were evaluated for various parameters such as hardness, friability, drug content, wetting time, dissolution study, disintegration test. An optimized formulation (GS-6) was found to have good hardness of 3.10 kg/cm2, disintegration time of 22.37 second and dissolution of 95 % in 12 min.The conclusion is results obtained clearly indicate that optimized batch GS-6 having remarkable increase in disintegrating and dissolution time for the treatment of depression

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal–organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal–organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated

PSO-Assisted Development of New Transferable Coarse-Grained Water Models

We have employed two-to-one mapping scheme to develop three coarse-grained (CG) water models, namely, 1-, 2-, and 3-site CG models. Here, for the first time, particle swarm optimization (PSO) and gradient descent methods were coupled to optimize the force-field parameters of the CG models to reproduce the density, self-diffusion coefficient, and dielectric constant of real water at 300 K. The CG MD simulations of these new models conducted with various timesteps, for different system sizes, and at a range of different temperatures are able to predict the density, self-diffusion coefficient, dielectric constant, surface tension, heat of vaporization, hydration free energy, and isothermal compressibility of real water with excellent accuracy. The 1-site model is ∼3 and ∼4.5 times computationally more efficient than 2- and 3-site models, respectively. To utilize the speed of 1-site model and electrostatic interactions offered by 2- and 3-site models, CG MD simulations of 1:1 combination of 1- and 2-/3-site models were performed at 300 K. These mixture simulations could also predict the properties of real water with good accuracy. Two new CG models of benzene, consisting of beads with and without partial charges, were developed. All three water models showed good capacity to solvate these benzene models