jmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data.

We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifically designed for the joint modelling of longitudinal and survival data in the context of big data analytics. The jmBIG package offers efficient and scalable implementations of joint modelling algorithms, allowing for integrating large-scale healthcare datasets.By utilizing the capabilities of jmBIG, researchers and analysts can effectively handle the challenges associated with big healthcare data, such as high dimensionality and complex relationships between multiple outcomes.With the support of jmBIG, analysts can seamlessly fit Bayesian joint models, generate predictions, and evaluate the performance of the models. The package incorporates cutting-edge methodologies and harnesses the computational capabilities of parallel computing to accelerate the analysis of large-scale healthcare datasets significantly. In summary, jmBIG empowers researchers to gain deeper insights into disease progression and treatment response, fostering evidence-based decision-making and paving the way for personalized healthcare interventions that can positively impact patient outcomes on a larger scale.

Alcohol Consumption, High-Density Lipoprotein Particles and Subspecies, and Risk of Cardiovascular Disease: Findings from the PREVEND Prospective Study.

The associations of HDL particle (HDL-P) and subspecies concentrations with alcohol consumption are unclear. We aimed to evaluate the interplay between alcohol consumption, HDL parameters and cardiovascular disease (CVD) risk. In the PREVEND study of 5151 participants (mean age, 53 years; 47.5% males), self-reported alcohol consumption and HDL-P and subspecies (small, medium, and large) by nuclear magnetic resonance spectroscopy were assessed. Hazard ratios (HRs) with 95% CIs for first CVD events were estimated. In multivariable linear regression analyses, increasing alcohol consumption increased HDL-C, HDL-P, large and medium HDL, HDL size, and HDL subspecies (H3P, H4P, H6 and H7) in a dose-dependent manner. During a median follow-up of 8.3 years, 323 first CVD events were recorded. Compared with abstainers, the multivariable adjusted HRs (95% CIs) of CVD for occasional to light, moderate, and heavy alcohol consumers were 0.72 (0.55-0.94), 0.74 (0.54-1.02), and 0.65 (0.38-1.09), respectively. These associations remained consistent on additional adjustment for each HDL parameter. For CVD, only HDL-C was associated with a statistically significant decreased risk of CVD in a fully adjusted analysis (HR 0.84, 95% CI 0.72-0.97 per 1 SD increment). For coronary heart disease, HDL-C, HDL-P, medium HDL, HDL size, and H4P showed inverse associations, whereas HDL-C and HDL size modestly increased stroke risk. Except for H6P, alcohol consumption did not modify the associations between HDL parameters and CVD risk. The addition of HDL-C, HDL size, or H4P to a CVD risk prediction model containing established risk factors improved risk discrimination. Increasing alcohol consumption is associated with increased HDL-C, HDL-P, large and medium HDL, HDL size, and some HDL subspecies. Associations of alcohol consumption with CVD are largely independent of HDL parameters. The associations of HDL parameters with incident CVD are generally not attenuated or modified by alcohol consumption.

Critically ill patients with diabetes and Middle East respiratory syndrome: a multi-center observational study.

BACKGROUND: Diabetes is a risk factor for infection with coronaviruses. This study describes the demographic, clinical data, and outcomes of critically ill patients with diabetes and Middle East Respiratory Syndrome (MERS). METHODS: This retrospective cohort study was conducted at 14 hospitals in Saudi Arabia (September 2012-January 2018). We compared the demographic characteristics, underlying medical conditions, presenting symptoms and signs, management and clinical course, and outcomes of critically ill patients with MERS who had diabetes compared to those with no diabetes. Multivariable logistic regression analysis was performed to determine if diabetes was an independent predictor of 90-day mortality. RESULTS: Of the 350 critically ill patients with MERS, 171 (48.9%) had diabetes. Patients with diabetes were more likely to be older, and have comorbid conditions, compared to patients with no diabetes. They were more likely to present with respiratory failure requiring intubation, vasopressors, and corticosteroids. The median time to clearance of MERS-CoV RNA was similar (23 days (Q1, Q3: 17, 36) in patients with diabetes and 21.0 days (Q1, Q3: 10, 33) in patients with no diabetes). Mortality at 90 days was higher in patients with diabetes (78.9% versus 54.7%, p < 0.0001). Multivariable regression analysis showed that diabetes was an independent risk factor for 90-day mortality (odds ratio, 2.09; 95% confidence interval, 1.18-3.72). CONCLUSIONS: Half of the critically ill patients with MERS have diabetes; which is associated with more severe disease. Diabetes is an independent predictor of mortality among critically patients with MERS.

Risk prediction by Raman spectroscopy for disease-free survival in oral cancers.

In the present study, the potential of Raman spectroscopy (RS) in predicting disease-free survival (DFS) in oral cancer patients has been explored. Raman spectra were obtained from the tumor and contralateral regions of 94 oral squamous cell carcinoma patients. These patients were managed surgically and recommended for adjuvant therapy. The Cox proportional survival analysis was carried out to identify the spectral regions that can be correlated to DFS. The survival analysis was performed with 95% confidence intervals, hazard ratio, and p-values in the 1200-1800 cm-1 spectral region. Out of a total of 182 spectral points, 76 were found to be correlating with DFS, suggesting their utility to predict the patient outcome. The cut-off points of each correlating RS-point values were defined and tested towards predicting the DFS. The performance of predicting the power of spectral points was validated through Brier value, and it was found to be closer to the actual progression. The 76 spectral points identified from the tumors have the potential to accurately predict DFS in oral squamous cell carcinoma through a relatively simplistic prediction model in the absence of confounding factors.

Human papillomavirus elevated genetic biomarker signature by statistical algorithm.

Head and neck squamous cell carcinoma (HNSCC) is the one of the most frequently found cancers in the world. The aim of the study was to find the genes responsible and enriched pathways associated with HNSCC using bioinformatics and survival analysis methods. A total of 646 patients with HNSCC based on clinical information were considered for the study. HNSCC samples were grouped according to the parameters (RFS, DFS, PFS, or OS). The probe ID of these 11 genes was retrieved by Affymetrix using the NetAffx Query algorithm. The protein-protein interaction (PPI) network and Kaplan-Meier curve were used to find associations among the genes' expression data. We found that among these 11 genes, nine genes, CCNA1, MMP3, FLRT3, GJB6, ZFR2, PITX2, SYCP2, MEI1, and UGT8 were significant (p < .05). A survival plot was drawn between the p value and gene expression. This study helped us find the nine significant genes which play vital roles in HNSCC along with their key pathways and their interaction with other genes in the PPI network. Finally, we found the biomarker index for relapse time and risk factors for HNSCC in cancer patients.

Transcriptomic, proteomic, and biochemical analyses reveal a novel neuritogenesis mechanism of Naja naja venom α-elapitoxin post binding to TrkA receptor of rat pheochromocytoma cells.

This is the first report showing unique neuritogenesis potency of Indian Cobra N. naja venom long-chain α-neurotoxin (Nn-α-elapitoxin-1) exhibiting no sequence similarity to conventional nerve growth factor, by high-affinity binding to its tyrosine kinase A (TrkA) receptor of rat pheochromocytoma (PC-12) cells without requiring low-affinity receptor p75NTR. The binding residues between Nn-α-elapitoxin-1 and mammalian TrkA receptor are predicted by in silico analysis. This binding results in a time-dependent internalization of TrkA receptor into the cytoplasm of PC-12 cells. The transcriptomic analysis has demonstrated the differential expression of 445 genes; 38 and 32 genes are up-regulated and down-regulated, respectively in the PC-12 cells post-treatment with Nn-α-elapitoxin-1. Global proteomic analysis in concurrence with transcriptomic data has also demonstrated that in addition to expression of a large number of common intracellular proteins in the control and Nn-α-elapitoxin-1-treated PC-12 cells, the latter cells also showed the expression of uniquely up-regulated and down-regulated intracellular proteins involved in diverse cellular functions. Altogether, the data from transcriptomics, proteomics, and inhibition of downstream signaling pathways by specific inhibitors, and the immunoblot analysis of major regulators of signaling pathways of neuritogenesis unambiguously demonstrate that, similar to mouse 2.5S-nerve growth factor, the activation of mitogen activated protein kinase/extracellular signal-regulated kinase is the major signaling pathway for neuritogenesis by Nn-α-elapitoxin-1. Nonetheless, fibroblast growth factor signaling and heterotrimeric G-protein signaling pathways were found to be uniquely expressed in Nn-α-elapitoxin-1-treated PC-12 cells and not in mouse 2.5S-nerve growth factor -treated cells. The TrkA binding region of Nn-α-elapitoxin-1 may be developed as a peptide-based drug prototype for the treatment of major central neurodegenerative diseases. Read the Editorial Highlight for this article on page 599.

Comparison of the seventh and eighth editions American Joint Committee Cancer classification system in oral cavity squamous cell cancers.

The aim of our study was to evaluate the predictive ability of the American Joint Committee Cancer (AJCC) eighth edition (AJCC8) staging system for oral cavity cancers and validate these changes rendering the hypothesis of improving prognostication. We conducted a retrospective study including all oral cavity squamous cell carcinoma patients visiting our tertiary center from 2012 to 2015, staged as per the AJCC seventh edition (AJCC7) and AJCC8 systems. Stage-specific disease-free survival (DFS) and overall survival (OS) were calculated using the Kaplan-Meier method. Concordance index (CI) and Akaike information criterion (AIC) were used to calculate the predictive accuracy of the both systems. The study sample consisted of 863 subjects followed up for a median of 24 months. Buccal mucosa complex (BMC) was the most common site (n = 496). We observed a 25.8% (n = 222) overall upstaging in the eighth edition, significantly seen in early tongue cancers (TCs) (Stage I) and advanced BMC cancers (Stage III). An increase in CI and reduction in AIC scores were indicative of a superior predictive accuracy for the eighth edition in assessing DFS (confidence interval [CI*] = 0.650-0.654; AIC = 3,022-3,014) and OS (CI* = 0.643-0.648; AIC = 2089-2086) across all stages. The accuracy was higher for TCs as compared to BMC. Although not statistically significant, we observed an increase in soft risk factors at higher stages in the eighth edition as compared to its predecessor. We concluded that the AJCC8 has a higher predictive accuracy than the AJCC7 edition, making it a reliable prognosticative tool.

HER2 borderline is a negative prognostic factor for primary malignant breast cancer.

BACKGROUND: HER-(human epidermal growth factor receptor 2) gene amplification and protein overexpression are important predictive, prognosis markers, and therapeutic target for breast cancer, emphasizing the importance of categorizing patients into HER2 positive and negative. However, from immunohistochemistry scores, 2% patients are neither HER2 + nor -ve, but borderline called HER2B. To make informed treatment decisions of these patients, it is important to know how different this group is compared to HER-2 positive/negative. METHODS: We analyzed n = 104,668 breast cancer patient samples from Surveillance, Epidemiology, and End Results (SEER) database. Survival analysis was performed using open source R (Cran project R version 3.5.0) "survival" package. Hazard ratio with confidence intervals was computed using coxph function. RESULTS: Of n = 104,668, 2239 (2.13%) patients were HER2 borderline, 87,157 (83.26%) HER2-negative, and 15,272 (14.6%) HER2-positive. The breast cancer as primary malignancy was observed in 84,944 (81.16%) patients. In primary malignant breast cancer (PMBC) patients, the hazard ratio among HER2-negative patients was significantly higher than HER2-positive patient samples (HR = 0.772, 95% CI 0.715-0.833, p = < .001), whereas HER2 negative status was not significantly favorable in PMBC negative patients in HER2-positive (HR = .919, 95% 0.797-1.06, p = .248). Most importantly in PMBC patients, the HR for HER2-borderline was poor in comparison to HER2 negative (HR = 1.354, 95% CI 1.126-1.627, p = < .001). CONCLUSION: This is the first report with large cohort of patient samples and significant statistical power to demonstrate that HER2 borderline represents a negative prognostic factor for PMBC. Thus providing rationale for controlled clinical trial for HER2-targeted therapies in HER2-borderline patients.

Classification algorithm for high-dimensional protein markers in time-course data.

Identification of biomarkers is an emerging area in oncology. In this article, we develop an efficient statistical procedure for the classification of protein markers according to their effect on cancer progression. A high-dimensional time-course dataset of protein markers for 80 patients motivates us for developing the model. The threshold value is formulated as a level of a marker having maximum impact on cancer progression. The classification algorithm technique for high-dimensional time-course data is developed and the algorithm is validated by comparing random components using both proportional hazard and accelerated failure time frailty models. The study elucidates the application of two separate joint modeling techniques using auto regressive-type model and mixed effect model for time-course data and proportional hazard model for survival data with proper utilization of Bayesian methodology. Also, a prognostic score is developed on the basis of few selected genes with application on patients. This study facilitates to identify relevant biomarkers from a set of markers.

Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling.

BACKGROUND: As the whole world is experiencing the cascading effect of a new pandemic, almost every aspect of modern life has been disrupted. Because of health emergencies during this period, widespread fear has resulted in compromised patient safety, especially for patients with cancer. It is very challenging to treat such cancer patients because of the complexity of providing care and treatment, along with COVID-19. Hence, an effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a huge challenge for the development of new methodology. METHODS: This article explores the time lag effect and makes a statistical inference about the best experimental arm using Accelerated Failure Time (AFT) model and regression methods. The work is presented as the occurrence of other events as a hazard rate after the first event (relapse). The time lag effect between the events is linked and analysed. RESULTS: The results were presented as a comprehensive analytical strategy by joining all disease progression. An AFT model applied with the transition states, and the dependency structure between the gap times was used by the auto-regression model. The effects of arms were compared using the coefficient of auto-regression and accelerated failure time (AFT) models. CONCLUSIONS: We provide the solutions to overcome the issue with intervals between two consecutive events in motivating head and neck cancer (HNC) data. COVID-19 is not going to leave us soon. We have to conduct several cancer clinical trials in the presence of COVID-19. A comprehensive analytical strategy to analyse cancer clinical trial data during COVID-19 pandemic is presented.

Design, Synthesis, and Biological Evaluation of 2-(2-Bromo-3-nitrophenyl)-5-phenyl-1,3,4-oxadiazole Derivatives as Possible Anti-Breast Cancer Agents.

Breast Cancer (BCa) is the most often diagnosed cancer among women who were in the late 1940's. Breast cancer growth is largely dependent on the expression of estrogen and progesterone receptor. Breast cancer cells may have one, both, or none of these receptors. The treatment for breast cancer may involve surgery, hormonal therapy (Tamoxifen, an aromatase inhibitor, etc.) and oral chemotherapeutic drugs. The molecular docking technique reported the findings on the potential binding modes of the 2-(2-bromo-3-nitrophenyl)-5-phenyl-1,3,4-oxadiazole derivatives with the estrogen receptor (PDB ID: 3ERT). The 1,3,4-oxadiazole derivatives 4a-4j have been synthesized and described by spectroscopic method. 2-(2-Bromo-6-nitrophenyl)-5-(4-bromophenyl)-1,3,4-oxadiazole (4c) was reconfirmed by single-crystal XRD. All the compounds have been tested in combination with generic Imatinib pharmaceutical drug against breast cancer cell lines isolated from Caucasian woman MCF-7, MDA-MB-453 and MCF-10A non-cancer cell lines. The compounds with the methoxy (in 4c) and methyl (in 4j) substitution were shown to have significant cytotoxicity, with 4c showing dose-dependent activation and decreased cell viability. The mechanism of action was reported by induced apoptosis and tested by a DNA enzyme inhibitor experiment (ELISA) for Methyl Transferase. Molecular dynamics simulations were made for hit molecule 4c to study the stability and interaction of the protein-ligand complex. The toxicity properties of ADME were calculated for all the compounds. All these results provide essential information for further clinical trials.

The results of sequential swallowing assessments after total laryngectomy for laryngeal and hypopharyngeal malignancies.

BACKGROUND: Swallowing after total laryngectomy (TL) is altered and the swallowing related issues are largely underreported. It is important to identify factors that may negatively influence swallowing after TL in order to rehabilitate these patients appropriately. METHODS: The study included patients who underwent TL from June 2015 to November 2017 for laryngeal and hypopharyngeal malignancy. Sequential swallowing assessment was done in these patients over time. The assessments were done using the FOIS scale and the PSS-HN normalcy of diet scores and analysed to assess the presence of swallowing related issues, factors influencing swallowing and its recovery over time. RESULTS: Sixty-seven who underwent total laryngectomy (TL) were included in the study. Swallowing assessments were done once in 3 months. Overall there was an improvement in swallowing over time. Both the FOIS (Median score of 3.82 in first to 5.77 in the fifth visit) and the PSS-HN scores (median score of 33.63 at first visit to 63.66 at fifth visit) improved over time. Patients undergoing TL after treatment failure with chemoradiotherapy (p value < 0.001) and those with advanced stage disease (p-value < 0.001) did poorly in terms of swallowing. At the last follow up only 8 patients were dependent on feeding tube; the rest of the patients were able to take food orally. CONCLUSION: Following total laryngectomy swallowing gradually improves in the first 18 months after surgery. It is essential to identify factors influencing swallowing negatively so that these patients can get appropriate attention to improve swallowing.

Alcohol and cancer risk: A systematic review and meta-analysis of prospective Indian studies.

BACKGROUND: Alcohol increases risk of cancer of oral cavity and pharynx, esophagus, colorectal, liver, larynx, and female breast. OBJECTIVES: The objective of this study was to determine the relationship of alcohol and cancer in India by meta-analysis. METHODS: Systematic Medline searches were performed to identify all the published literature associating alcohol and cancer in India. Initially, we retrieved 1509 studies, but after applying inclusion and exclusion criteria, only 29 studies were found eligible for our meta-analysis. RESULTS: Our meta-analysis shows that alcohol consumption increases the risk of cancer with the odds ratio (OR) of 2.32 (95% confidence interval [CI]: 1.50-3.47) in case-control studies and relative risk of 1.52 (95% CI: 0.97-2.51) in cohort studies. It also shows that risk of oral cavity cancer increases by two times (OR: 1.92, 95% CI: 1.54-3.96) in the population consuming alcohol. Publication analysis showed that studies included in the meta-analysis had wide variation, suggesting good representation all over the country. CONCLUSION: The result from our meta-analysis supports our hypothesis that alcohol consumption increases the risk of cancer, implying immediate cessation of the habit for cancer risk reduction.

Bioprospection of anti-inflammatory phytochemicals suggests rutaecarpine and quinine as promising 15-lipoxygenase inhibitors.

15-Lipoxygenase (15-LOX) belongs to the family of nonheme iron containing enzymes that catalyzes the peroxidation of polyunsaturated fatty acids (PUFAs) to generate eicosanoids that play an important role in signaling pathways. The role of 15-LOX has been demonstrated in atherosclerosis as well as other inflammatory diseases. In the present study, drug-like compounds were first screened from a set of anti-inflammatory phytochemicals based on Lipinski's rule of five (ROF) and in silico toxicity filters. Two lead compounds-quinine (QUIN) and rutaecarpine (RUT) were shortlisted by analyzing molecular interactions and binding energies of the filtered compounds with the target using molecular docking. Molecular dynamics simulation studies indicate stable trajectories of apo_15-LOX and docked complexes (15-LOX_QUIN and 15-LOX_RUT). In vitro 15-LOX inhibition studies shows that both QUIN and RUT have lower inhibitory concentration (IC50 ) value than the control (quercetin). Both QUIN and RUT exhibit moderate antioxidant activities. The cell viability study of these compounds suggests no significant toxicity in HEK-293 cell lines. Further, QUIN and RUT both did not show any inhibition against selected Gram-positive and Gram-negative bacterial species. Thus, based on our present findings, rutaecarpine and quinine may be suggested as promising 15-LOX inhibitor for the prevention of the atherosclerosis development.

A randomized phase 3 trial comparing nimotuzumab plus cisplatin chemoradiotherapy versus cisplatin chemoradiotherapy alone in locally advanced head and neck cancer.

BACKGROUND: Because the addition of nimotuzumab to chemoradiation in patients with locally advanced head and neck cancer improved outcomes in a phase 2 study, the authors conducted a phase 3 study to confirm these findings. METHODS: This open-label, investigator-initiated, phase 3, randomized trial was conducted from 2012 to 2018. Adult patients with locally advanced head and neck cancer who were fit for radical chemoradiation were randomized 1:1 to receive either radical radiotherapy (66-70 grays) with concurrent weekly cisplatin (30 mg/m2 ) (CRT) or the same schedule of CRT with weekly nimotuzumab (200 mg) (NCRT).The primary endpoint was progression-free survival (PFS); key secondary endpoints were disease-free survival (DFS), duration of locoregional control (LRC), and overall survival (OS). An intent-to-treat analysis also was performed. RESULTS: In total, 536 patients were allocated equally to both treatment arms. The median follow-up was 39.13 months. The addition of nimotuzumab improved PFS (hazard ratio [HR], 0.69; 95% CI, 0.53-0.89; P = .004), LRC (HR, 0.67; 95% CI, 0.50-0.89; P = .006), and DFS (HR, 0.71; 95% CI, 0.55-0.92; P = .008) and had a trend toward improved OS (HR, 0.84; 95% CI, 0.65-1.08; P = .163). Grade 3 through 5 adverse events were similar between the 2 arms, except for a higher incidence of mucositis in the NCRT arm (66.7% vs 55.8%; P = .01). CONCLUSIONS: The addition of nimotuzumab to concurrent weekly CRT improves PFS, LRC, and DFS. This combination provides a novel alternative therapeutic option to a 3-weekly schedule of 100 mg/m2 cisplatin in patients with locally advanced head and neck cancer who are treated with radical-intent CRT.

Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method.

BACKGROUND: SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification. METHODS: The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1. RESULTS: Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally. CONCLUSION: This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

Impact of a non-synonymous Q281R polymorphism on structure of human Lipoprotein-Associated Phospholipase A2 (Lp-PLA2 ).

Non-synonymous single nucleotide polymorphisms (nsSNPs) are genetic variations at single base resulting in an amino acid change which have been associated with various complex human diseases. The human Lipoprotein-associated phospholipase A2 (Lp-PLA2 ) gene harbours a rare Q281R polymorphism which was previously reported to cause loss of enzymatic function. Lp-PLA2 is an important enzyme which catalyzes the hydrolysis of polar phospholipids releasing pro-atherogenic and pro-inflammatory mediators involved in the pathogenesis of atherosclerosis. Our current study is aimed at elucidating the structural and functional consequences of Q281R polymorphism on Lp-PLA2 . The Q281R mutation is classified as deleterious and causes protein instability as deduced from evolutionary, folding free energy changes and Support vector machine (SVM)-based methods. A Q281R mutant structure was deciphered using homology modelling approach and was validated using phi and psi dihedral angles distribution, ERRAT, Verify_3D scores, Protein Structure Analysis (ProSA) energ,y and Z-score. A decreased hydrophobic interactions and weaker substrate binding affinity was observed in the mutant compared to the wild- type (WT) using molecular docking. Further, the mutant displayed enhanced structural flexibility particularly in the low density lipoprotein (LDL) binding domain, decreased solvent accessibility of catalytic residues-Phe274 and Ser273 and increased Cɑ distance between Phe274 and Leu153 and large conformational entropy change as inferred from all-atom molecular dynamics (MD) simulation and essential dynamics (ED) studies. Our results corroborate well with previous experimental studies and thus these aberrations in the Q281R mutant structure may help explain the molecular basis of loss of enzyme activity.

Assessing the potential of Psidium guajava derived phytoconstituents as anticholinesterase inhibitor to combat Alzheimer's disease: an in-silico and in-vitro approach.

Acetylcholinesterase (AChE) inhibitors play a crucial role in the treatment of Alzheimer's disease. These drugs increase acetylcholine levels by inhibiting the enzyme responsible for its degradation, which is a vital neurotransmitter involved in memory and cognition. This intervention intermittently improves cognitive symptoms and augments neurotransmission. This study investigates the potential of Psidium guajava fruit extract as an acetylcholinesterase (AChE) inhibitor for Alzheimer's disease treatment. Molecular characteristics and drug-likeness were analyzed after HR-LCMS revealed phytocompounds in an ethanolic extract of Psidium guajava fruit. Selected phytocompounds were subjected to molecular docking against AChE, with the best-docked compound then undergoing MD simulation, MMGBSA, DCCM, FEL, and PCA investigations to evaluate the complex stability. The hit compound's potential toxicity and further pharmacokinetic features were also predicted. Anticholinesterase activity was also studied using in vitro assay. The HR-LCMS uncovered 68 compounds. Based on computational analysis, Fluspirilene was determined to have the highest potential to inhibit AChE. It was discovered that the Fluspirilene-AChE complex is stable and that Fluspirilene has a high binding affinity for AChE. Extract of Psidium guajava fruit significantly inhibits AChE (88.37% at 200 µg/ml). It is comparable to the standard AChE inhibitor Galantamine. Fluspirilene exhibited remarkable binding to AChE. Psidium guajava fruit extract demonstrated substantial AChE inhibitory activity, indicating its potential for Alzheimer's treatment. The study underscores natural sources' significance in drug discovery.Communicated by Ramaswamy H. Sarma.

Competing risk multistate censored data modeling by propensity score matching method.

The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.

Walking pace and the time between the onset of noncommunicable diseases and mortality: a UK Biobank prospective cohort study.

PURPOSE: To estimate time spent in various cardiovascular disease (CVD) and cancer states, according to self-reported walking pace. METHODS: In total, 391,744 UK Biobank participants were included (median age = 57 years; 54.7% women). Data were collected 2006-2010, with follow-up collected in 2021. Usual walking pace was self-defined as slow, steady, average, or brisk. Multistate modeling determined the transition rate and mean sojourn time in and across three different states (healthy, CVD or cancer, and death) upon a time horizon of 10 years. RESULTS: The mean sojourn time in the healthy state was longer, while that in the CVD or cancer state was shorter in individuals reporting an average or brisk walking pace (vs. slow). A 75-year-old woman reporting a brisk walking pace spent, on average, 8.4 years of the next 10 years in a healthy state; an additional 8.0 (95% CI: 7.3, 8.7) months longer than a 75-year-old woman reporting a slow walking pace. This corresponded to 4.3 (3.7, 4.9) fewer months living with CVD or cancer. Similar results were seen in men. CONCLUSIONS: Adults reporting an average or brisk walking pace at baseline displayed a lower transition to disease development and a greater proportion of life lived without CVD or cancer. AVAILABILITY OF DATA AND MATERIALS: Research was conducted using the UK Biobank resource under Application #33266. The UK Biobank resource can be accessed by researchers on application. Variables derived for this study have been returned to the UK Biobank for future applicants to request. No additional data are available.