Model simplification and sensitivity analysis of chemical species for oxidation behavior in multi route complex chemical mechanism.

This study employs a Model Reduction Technique (MRT) to simplify the four-step catalytic carbon monoxide (CO) oxidation reaction. The C-matrix method identifies key elements, key/non key components, and key reactions, while the Intrinsic Low-Dimensional Manifold (ILDM) pinpoints a Slow-Invariant Manifold (SIM) important for understanding key species behavior. Sensitivity analysis can be considered for measuring the efficiency of the chemical species in detailed mechanism. This systematic approach contributes to optimizing and controlling complex reactions offering broad application potential. In addition to the mathematical proof, the validation of the given chemical model is rectified. The comparison between the slow invariant manifold of both reaction routes is reported and the computational based results performed in this study are obtained through MATLAB.

Gender Matters: A Multidimensional Approach to Optimizing Cardiovascular Health in Women.

Cardiovascular diseases remain a leading cause of mortality among women, yet they are often underestimated and insufficiently addressed. This narrative review delves into the gender disparities in cardiovascular health, underscoring the critical importance of recognizing and addressing the unique challenges women face. The article explores the pathophysiological differences between men and women, highlighting the role of hormonal factors, such as estrogen and menopause, in conferring cardioprotection or increasing risk. It examines the complexities of diagnosis and assessment, including differences in symptom presentation, diagnostic accuracy, and the challenges of interpreting non-invasive testing in women. The review also highlights the need for tailored risk assessment and prevention strategies, incorporating sex-specific conditions and pregnancy-related factors. It emphasizes the importance of lifestyle modifications and interventions, as well as the potential benefits of personalized treatment approaches, considering gender-specific variations in medication responses and cardiac interventions. Furthermore, the article sheds light on the impact of psychosocial and sociocultural factors, such as gender norms, mental health considerations, and access to healthcare, on women's cardiovascular health. It also addresses the significant gaps and challenges in research, including the historical underrepresentation of women in clinical trials and the lack of sex- and gender-sensitive studies. Finally, the review advocates for a multidisciplinary approach, involving patient-centered care, shared decision-making, and collaboration among policymakers, stakeholders, and healthcare systems. This comprehensive strategy aims to enhance awareness, prevention, diagnosis, and treatment of cardiovascular disease in women, ultimately improving health outcomes and reducing the burden of this often overlooked epidemic.

Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches.

Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.

SERS characterization of biochemical changes associated with biodesulfurization of dibenzothiophene using Gordonia sp. HS126-4N.

In this study, Gordonia sp. HS126-4N was employed for dibenzothiophene (DBT) biodesulfurization, tracked over 9 days using SERS. During the initial lag phase, no significant spectral changes were observed, but after 48 h, elevated metabolic activity was evident. At 72 h, maximal bacterial population correlated with peak spectrum variance, followed by stable spectral patterns. Despite 2-hydroxybiphenyl (2-HBP) induced enzyme suppression, DBT biodesulfurization persisted. PCA and PLS-DA analysis of the SERS spectra revealed distinctive features linked to both bacteria and DBT, showcasing successful desulfurization and bacterial growth stimulation. PLS-DA achieved a specificity of 95.5 %, sensitivity of 94.3 %, and AUC of 74 %, indicating excellent classification of bacteria exposed to DBT. SERS effectively tracked DBT biodesulfurization and bacterial metabolic changes, offering insights into biodesulfurization mechanisms and bacterial development phases. This study highlights SERS' utility in biodesulfurization research, including its use in promising advancements in the field.

Photonic Pigments of Polystyrene-block-Polyvinylpyrrolidone Bottlebrush Block Copolymers via Sustainable Organized Spontaneous Emulsification.

Prior studies on photonic pigments of amphiphilic bottlebrush block copolymers (BBCPs) through an organized spontaneous emulsification (OSE) mechanism have been limited to using polyethylene glycol (PEG) as the hydrophilic side chains and toluene as the organic phase. Herein, a family of polystyrene-block-polyvinylpyrrolidone (PS-b-PVP) BBCPs are synthesized with PVP as the hydrophilic block. Biocompatible and sustainable anisole is employed for dissolving the obtained BBCPs followed by emulsification of the solutions in water. Subsequent evaporation of oil-in-water emulsion droplets triggers the OSE mechanism, producing thermodynamically stable water-in-oil-in-water (w/o/w) multiple emulsions with uniform and closely packed internal droplet arrays through the assembly of the BBCPs at the w/o interface. Upon solidification, the homogeneous porous structures are formed within the photonic microparticles that exhibit visible structural colors. The pore diameter is widely tunable (150∼314 nm) by changing the degree of polymerization of BBCP (69∼110), resulting in tunable colors across the whole visible spectrum. This work demonstrates useful knowledge that OSE can be generally used in the fabrication of ordered porous materials with tunable internal functional groups, not only for photonic applications, but also offers a potential platform for catalysis, sensing, separation, encapsulation, etc.

The Effectiveness of Transcranial Magnetic Stimulation in Treating Apraxia.

Apraxia can be detected when engaging in mental motor envisioning exercises. The nonverbal skills of manufacturing, representation, strategizing, arithmetic, visual sensitivity, and motor skills are all related to apraxia. Limb apraxia also negatively affects communication gestures and linguistic skills. The impairment of brain regions related to motion patterns is the primary cause of apraxia. People with apraxia may struggle to complete a variety of tasks because they are unable to focus on various movements. Apraxia can result from injury to the premotor cortex since it has a role in the left hemisphere-dependent selection of movements. Cognitive and complicated motor system deficits are hallmarks of the corticobasal syndrome. Apraxia of the limbs and visuospatial abnormalities are typical clinical types. TMS was used to study these problems; however, no research was done on the relationship between TMS parameters and clinical types. It is possible for changes in brain activity to last a long time when repetitive TMS (rTMS) is utilized. Electromyography shows that noninvasive TMS of the motor cortex causes target muscle spasms (MEP). The human motor cortex is a part of the cerebral cortex that is involved in the organization, management, and execution of voluntary movements. TMS and other neuroimaging techniques are frequently used to identify changes in this region. Cortical motor excitability varies among different diagnoses; therefore, it is important to determine the effectiveness of TMS. Therefore, this study aims to review the causes and neurophysiological simulation of apraxia along with the principles and effects of TMS on apraxia.

Characterization of structural changes occurring in insulin at different time intervals at room temperature by surface-enhanced Raman spectroscopy.

BACKGROUND: Insulin storage above the temperature recommended by food and drug administration (FDA) causes decrease in its functional efficacy due to degradation and aggregation of its protein based active pharmaceutical ingredient (API) that results poor glycemic control in diabetic patients. The aggregation of protein causes serious neurodegenerative diseases such as type-2 diabetes, Huntington disease, Parkinson's disease, and Alzheimer's disease. Surface-enhanced Raman spectroscopy (SERS) has been employed for the denaturation study of many proteins at the temperature above the recommendations of food and drug administration (FDA) (above 30 °C) which indicates potential of technique for such studies. OBJECTIVE: SERS along with multivariate discriminating analysis techniques-based analysis of degradation of liquid pharmaceutical insulin protein after regular intervals of time at room temperature to analyze the structural changes in this protein during the storage of insulin pharmaceutical at room temperature. METHODS: Silver nanoparticles (Ag-NPs) prepared by chemical reduction method are used as SERS active substrate for the surface enhancement of the insulin spectral signal. SERS spectral measurements of insulin were collected from eight different samples of insulin in the time range of 7 pm to 7 am first at fridge temperature (5 °C), second after half hour and next six with the time difference of 2 h each time at room temperature. The acquired SERS spectral data was preprocessed and analyzed. SERS structural transformations detection and discrimination potential in insulin was further confirmed by applying multivariate discriminating analysis techniques including principal component analysis (PCA) and Partial least square regression analysis (PLSR). RESULTS: SERS significantly detects the structural changes produced in insulin even after 2 h of insulin placement at room temperature. PCA successfully differentiates the insulin spectral data obtained after regular intervals of time according to PC-1 (77 %) explained variance. Application of PLSR model provides quantitative confirmation of SERS efficiency, by providing insulin data regression coefficients plot, efficient prediction of time with calibration data set having 0.77 mean square absolute error of calibration (RMSAEC), validation data set with 0.80 mean square absolute error of prediction (RMSAEP) and 0.98 coefficient of determination (R2) for both calibration and validation data set. CONCLUSION: SERS is proved as a highly sensitive and discriminating technique to detect and discriminate insulin structural changes after regular intervals of time at room temperature.

Tackling microplastics pollution in global environment through integration of applied technology, policy instruments, and legislation.

Microplastic pollution is a serious environmental problem that affects both aquatic and terrestrial ecosystems. Small particles with size of less than 5 mm, known as microplastics (MPs), persist in the environment and pose serious threats to various species from micro-organisms to humans. However, terrestrial environment has received less attention than the aquatic environment, despite being a major source of MPs that eventually reaches water body. To reflect its novelty, this work aims at providing a comprehensive overview of the current state of MPs pollution in the global environment and various solutions to address MP pollution by integrating applied technology, policy instruments, and legislation. This review critically evaluates and compares the existing technologies for MPs detection, removal, and degradation, and a variety of policy instruments and legislation that can support the prevention and management of MPs pollution scientifically. Furthermore, this review identifies the gaps and challenges in addressing the complex and diverse nature of MPs and calls for joint actions and collaboration from stakeholders to contain MPs. As water pollution by MPs is complex, managing it effectively requires their responses through the utilization of technology, policy instruments, and legislation. It is evident from a literature survey of 228 published articles (1961-2023) that existing water technologies are promising to remove MPs pollution. Membrane bioreactors and ultrafiltration achieved 90% of MPs removal, while magnetic separation was effective at extracting 88% of target MPs from wastewater. In biological process, one kg of wax worms could consume about 80 g of plastic/day. This means that 100 kg of wax worms can eat about 8 kg of plastic daily, or about 2.9 tons of plastic annually. Overall, the integration of technology, policy instrument, and legislation is crucial to deal with the MPs issues.

Surface-enhanced Raman spectroscopy for studying the interaction of N-propyl substituted imidazole compound with salmon sperm DNA.

BACKGROUND: Surface Enhanced Raman Spectroscopy (SERS) is a very promising and fast technique for studying drugs and for detecting chemical nature of a molecule and DNA interaction. In the current study, SERS is employed to check the interaction of different concentrations of n-propyl imidazole derivative ligand with salmon sperm DNA using silver nanoparticles as SERS substrates. OBJECTIVES: Multivariate data analysis technique like principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) are employed for the detailed analysis of the SERS spectral features associated with the mode of action of the imidazole derivative ligand with DNA. METHODOLOGY: Silver nanoparticles were used as a SERS substrate in DNA-drug interaction. Five different concentrations of ligands were interacted with DNA and mix with Ag-NPs as substrate. The SERS spectra of were acquired for all seven samples and processed using MATLAB. Additionally, PCA and PLS-DA were used to assessed the ability SERS to differentiate interaction of DNA-drug. RESULTS: Differentiating SERS features having changes in their peak position and intensities are observed including 629, 655, 791, 807, 859, 1337, 1377 and 1456 cm-1. These SERS features reveal that binding of ligand with DNA is electrostatic in nature, and have specificity to major groove where it forms GC-CG interstrand cross-linking with the DNA double helix. CONCLUSIONS: SERS give significant information regarding to Drug-DNA interaction mechanism, SERS spectra inferred the mode of action of anticancer compound that are imidazole in nature.

Bacillus thuringiensis CHGP12 uses a multifaceted approach for the suppression of Fusarium oxysporum f. sp. ciceris and to enhance the biomass of chickpea plants.

BACKGROUND: Bacillus species synthesize antifungal lipopeptides (LPs) making them a sustainable and eco-friendly management option to combat Fusarium wilt of chickpea. RESULTS: In this study, 18 endophytic Bacillus strains were assessed for their antifungal activity against Fusarium oxysporum f. sp. ciceris (FOC) associated with Fusarium wilt of chickpea. Among them, 13 strains produced significant inhibition zones in a direct antifungal assay while five strains failed to produce the inhibition of FOC. Bacillus thuringiensis CHGP12 exhibited the highest inhibition 3.45 cm of FOC. The LPs extracted from CHGP12 showed significant inhibition of the pathogen. Liquid chromatography-mass spectrometry (LC-MS) analysis confirmed that CHGP12 possessed the ability to produce fengycin, surfactin, iturin, bacillaene, bacillibactin, plantazolicin, and bacilysin. In an in vitro qualitative assay CHGP12 exhibited the ability to produce lipase, amylase, cellulase, protease, siderophores, and indole 3-acetic acid (IAA). IAA and gibberellic acid (GA) were quantified using ultra-performance liquid chromatography (UPLC) with 370 and 770 ng mL-1 concentrations of IAA and GA respectively. Furthermore, the disease severity showed a 40% decrease over control in CHGP12 treated plants compared to the control in a glasshouse experiment. Moreover, CHGP12 also exhibited a significant increase in total biomass of the plants namely, root and shoot growth parameters, stomatal conductance, and photosynthesis rate. CONCLUSION: In conclusion, our findings suggest that B. thuringiensis CHGP12 is a promising strain with high antagonistic and growth-promoting potential against Fusarium wilt of chickpea. © 2022 Society of Chemical Industry.

Cognition improvement strategies, study behaviours and stress among medical students of Dow University of Health Sciences: A cross-sectional study.

OBJECTIVE: To determine the effects of cognition improvement strategies on academic performance, stress and sleep quality of medical students. METHODS: The cross-sectional study was conducted at the Dow University of Health Sciences, Karachi, from March 2019 to March 2020, and comprised medical students regardless of gender and academic year. Academic performance was assessed through grade point average, while stress and sleep were assessed using the Perceived Stress Scale and Pittsburgh Sleep Quality Index. Data was analysed using SPSS 25. RESULTS: Of the 770 subjects, 748(97%) formed the final sample with mean age 20.32±1.49 years; 619(82.7%) females and 129(17.2%) males. Overall, there were 655(87.6%) consumers of cognitive enhancers and 93(12.4%) non-cunsumers. The mean Pittsburgh Sleep Quality Index score of consumers was 6.05±3.306, while that of non-consumers was 5.80±3.701. The respective mean Perceived Stress Scale scores were 21.18±6.09 and 20.5±6.8. There was no significant association of consumption of cognitive enhancers with academic performance and stress levels (p>0.05), but it was significant with sleep quality (p<0.05). CONCLUSIONS: Majority of the students were found to be consuming cognitive enhancers, but no significant association of the stimulants was found with either academic performance or stress.

Recent advances in bioremediation of heavy metals and persistent organic pollutants: A review.

Heavy metals and persistent organic pollutants are causing detrimental effects on the environment. The seepage of heavy metals through untreated industrial waste destroys the crops and lands. Moreover, incineration and combustion of several products are responsible for primary and secondary emissions of pollutants. This review has gathered the remediation strategies, current bioremediation technologies, and their primary use in both in situ and ex situ methods, followed by a detailed explanation for bioremediation over other techniques. However, an amalgam of bioremediation techniques and nanotechnology could be a breakthrough in cleaning the environment by degrading heavy metals and persistant organic pollutants.

Systematic review of the application of virtual reality to improve balance, gait and motor function in patients with Parkinson's disease.

BACKGROUND: Virtual reality (VR) is an advanced technique used in physical rehabilitation of neurological disorders, however the effects of VR on balance, gait, and motor function in people with Parkinson's (PD) are still debated. Therefore, the systematic review aimed to determine the role of VR on motor function, balance and gait in PD patients. METHODS: A comprehensive search to identify similar randomised controlled trials was conducted targeting 5 databases including Web of Science, PubMed, CINHAL, Cochrane Library, and Physiotherapy Evidence Database. A total of 25 studies were found eligible for this systematic review, and the methodological assessment of the quality rating of the studies was accomplished using the physiotherapy evidence database scale by 2 authors. RESULTS: Out of the 25 included studies, 14 studies reported on balance as the primary outcome, 9 studies were conducted to assess motor function, and 12 assessed gait as the primary outcome. Most studies used the Unified Parkinson disease rating scale UPDRS (part-III) for evaluating motor function and the Berg Balance Scale as primary outcome measure for assessing balance. A total of 24 trials were conducted in clinical settings, and only 1 study was home-based VR trainings. Out of 9 studies on motor function, 6 reported equal improvement of motor function as compared to other groups. In addition, VR groups also revealed superior results in improving static balance among patient with PD. CONCLUSION: This systemic review found that the use of VR resulted in substantial improvements in balance, gait, and motor skills in patients with PD when compared to traditional physical therapy exercises or in combination with treatments other than physical therapy. Moreover, VR can be used as a supportive method for physical rehabilitation in patients of PD. However, the majority of published studies were of fair and good quality, suggesting a demand for high quality research in this area.

Predisposition of Early Maladaptive Schemas and Postpartum Depression during COVID-19 Crisis: Mediation of Mindfulness.

BACKGROUND: Past studies provide crucial evidence that pregnancy and childbirth increase the risk of emotional vulnerability and instability. Current research intends to explore the role of early maladaptive schemas and mindfulness as determinants of postpartum depression for expecting mothers during COVID-19 pandemic. METHOD: The data was collected from 170 expecting mothers who conceived and gave birth during the COVID-19 pandemic. Edinburgh postnatal depression scale, the young schema questionnaire-SF 75 items, and the Kentucky inventory for mindfulness scale were administered. RESULTS: Results discovered that mindfulness partially mediated the relationship between three kinds of early maladaptive schemas and postpartum depression. CONCLUSION: Mindfulness-based control techniques can be considered to buffer the impact of the early maladaptive schemas on postpartum depression, for mothers who give birth during any challenging time.

Surface-enhanced Raman spectroscopy of polymerase chain reaction (PCR) products of Rifampin resistant and susceptible tuberculosis patients.

BACKGROUND: Raman spectroscopy is an effective tool for detecting and discriminating microorganisms that is robust, reliable, and rapid. OBJECTIVES: To develop a polymerase chain reaction technique (PCR) based on Surface Enhanced Raman Spectroscopy (SERS) technique with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to assess diagnostic capability of SERS for distinguishing between tuberculosis (TB) positive rifampin resistant and tuberculosis (TB) positive rifampin susceptible samples. METHODS: Silver nanoparticles (Ag NPs) were used as SERS substrates and technique was used to distinguish TB positive rifampin (RIF) resistant and TB positive rifampin (RIF) susceptible patients on the basis of characteristic SERS spectral features of their respective PCR products. SERS spectra were acquired from 52 samples of PCR products including 22 samples of TB positive rifampin susceptible, 30 samples of TB positive rifampin resistant and negative control samples. All these samples were collected from individuals of same age. Furthermore, multivariate data analyses techniques such as PCA and PLS-DA were used to assess diagnostic capability of SERS for distinguishing between TB positive rifampin resistant and TB positive rifampin susceptible samples. RESULTS: PCA is found helpful for successful differentiation among these two groups of spectral data sets. Moreover, PLS-DA provides this classification quantitatively by predicting the class of SERS spectral data set with 73% area under curve, 96% sensitivity, 95.6% specificity and 95% accuracy. CONCLUSION: SERS can be employed for the rapid distinguishing between TB positive rifampin resistant and TB positive rifampin susceptible samples.

De novo synthesis of photocatalytic bifunctional MIL-125(Ti)/gC3N4/RGO through sequential self-assembly and solvothermal route.

In this study we have synthesized a heterostructured metal organic framework (MOF) consisting of self-assembled porous carbon nitride (gC3N4) and, reduced graphene oxide (RGO) with MIL-125(Ti) (CN-GO-MIL) through a simple synthesis route. As-synthesized CN-GO-MIL was characterized to determine its morphological, surface, structural, and optical properties. The synthesis produced a porous nanomaterial with efficient visible light capture and electron transport. CN-GO-MIL proved 2.23 and 1.23 times as effective as bare MIL-125(Ti) for Rhodamine B (RhB) degradation and chromium (Cr) reduction, respectively. We propose a governing photocatalytic degradation and reduction mechanism in which superoxide plays a major role in the photocatalytic degradation, followed by O21, OH·, and holes, and identify methanol as a suitable hole scavenger for reduction of Cr. Moreover, Cr reduction can be best achieved at pH 2 in the presence of methanol. Performance of material in terms of apparent quantum yield (AQY), figure of merit (FOM), and catalyst surface efficiency (S.E), suggests 5% CN-GO-MIL is an efficient photocatalyst for degradation of RhB. Comparison of the AQY with previously reported MOF-based composites shows that the as synthesized 5% CN-GO-MIL can be regarded as one of best performing photocatalyst under visible light irradiation for abatement of organic and inorganic pollution.

Granulocyte to Lymphocyte Ratio among Different Categories of Neonatal Sepsis according to their Vitamin D Status.

The objective of this study was to ascertain whether, among the cases of neonatal sepsis, there is any significant difference between GLR (Granulocyte to Lymphocyte ratio) of different groups of patients according to their vitamin D status. One hundred and nine neonates with odd admission number, admitted in NICU (Neonatal Intensive Care Unit) with clinical manifestations of neonatal sepsis during the study period from December 2017 to December 2018, were included in the study. Vitamin D deficiency was present in 83 (76.1%), normal vitamin D levels were present in 21 (19.3%), and hypervitaminosis D was detected in 5 (4.6%) patients. Kruskal-Wallis test showed that there was no significant difference between GLR of different group of patients, according to their vitamin D status. There was no significant difference between GLR of different groups, according to outcome. Key Words: Granulocyte to Lymphocyte Ratio, Vitamin D, Neonatal sepsis.

In Silico Mutagenesis-Based Remodelling of SARS-CoV-1 Peptide (ATLQAIAS) to Inhibit SARS-CoV-2: Structural-Dynamics and Free Energy Calculations.

The prolific spread of COVID-19 caused by a novel coronavirus (SARS-CoV-2) from its epicenter in Wuhan, China, to every nook and cranny of the world after December 2019, jeopardize the prevailing health system in the world and has raised serious concerns about human safety. Multi-directional efforts are made to design small molecule inhibitors, and vaccines and many other therapeutic options are practiced, but their final therapeutic potential is still to be tested. Using the old drug or vaccine or peptides could aid this process to avoid such long experimental procedures. Hence, here, we have repurposed a small peptide (ATLQAIAS) from the previous study, which reported the inhibitory effects of this peptide. We used in silico mutagenesis approach to design more peptides from the native wild peptide, which revealed that substitutions (T2W, T2Y, L3R, and A5W) could increase the binding affinity of the peptide towards the 3CLpro. Furthermore, using MD simulation and free energy calculation confirmed its dynamics stability and stronger binding affinities. Per-residue energy decomposition analysis revealed that the specified substitution significantly increased the binding affinity at the residue level. Our wide-ranging analyses of binding affinities disclosed that our designed peptide owns the potential to hinder the SARS-CoV-2 and will reduce the progression of SARS-CoV-2-borne pneumonia. Our research strongly suggests the experimental and clinical validation of these peptides to curtail the recent corona outbreak.