The Role of Neutrophil to Lymphocyte Ratio and its Common Clinical Outcomes Among Patients with Non-ST Elevation Acute Coronary Syndrome.

Objectives: To evaluate the admission neutrophil-to-lymphocyte ratio (NLR) for risk stratification for in-hospital outcomes and complications in non-ST-elevation acute coronary syndrome (non-ST-ACS) patients. Methods: We recruited consecutive patients with non-ST-ACS. The NLR was obtained and stratified as low, intermediate, and high-risk based on <3.0, 3.0-6.0, and >6.0, respectively. The new ST-T changes, arrhythmias, contrast-induced nephropathy (CIN), and mortality were recorded. Results: Median NLR was 3 [2.1-5.3] for 346 patients with 19.9% and 30.6% in high- and intermediate-risk group. New ST-T changes were observed in 3.5% (12) out of which 8, 3, and 1 patient in low, intermediate, and high-risk group (p = 0.424), respectively. Arrhythmias were observed in 5.8% (20) with 7, 5, and 8 patients in low, intermediate, and high-risk group (p = 0.067), respectively. CIN was observed in 4.9% (17) with 5, 5, and 7 in low, intermediate, and high-risk group (p = 0.064), respectively. In-hospital mortality was recorded in 1.4% (5) with 2 and 3 patients in high and low-risk group (p = 0.260), respectively. Conclusion: A significant number of non-ST-ACS patients fall in the high-risk category of NLR. Although, the association between NLR and in-hospital mortality and adverse events was not statistically significant but relatively higher rates of events were observed in high risk group.

Retraction: Mortality Risk Factors for Patients With Sepsis-Induced Blood Pressure Drop: A Single-Center Retrospective Study.

[This retracts the article DOI: 10.7759/cureus.22114.].

Mortality Risk Factors for Patients With Sepsis-Induced Blood Pressure Drop: A Single-Center Retrospective Study.

INTRODUCTION: Sepsis is a life-threatening illness caused by the body's response to uncontrolled infection. Different studies have been conducted to identify risk factors associated with the diagnosis of sepsis and mortality, but there has been considerably less focus on mortality due to sepsis-induced blood pressure. The current study was conducted to determine the incidence of mortality within 30 days among patients with sepsis-induced blood pressure drop and its risk factors. METHODOLOGY: It was a retrospective study conducted at the Pakistan Navy Station (PNS) Shifa Hospital, Karachi, Pakistan. Data of all patients aged 18 years or more who visited PNS Shifa Hospital and were diagnosed with sepsis and blood pressure reduction from November 2019 to October 2021 were extracted from Hospital Management Information System (HMIS) and retrospectively analyzed Results: All variables significantly associated with 30-days mortality in multivariable logistic regression analysis, including disturbance of consciousness, cardiac insufficiency, respiratory failure, diabetes mellitus, creatinine level, and aspartate aminotransferase (AST) level, were risk factors for mortality in patients with the sepsis-induced drop in blood pressure (p-value<0.05). CONCLUSION: Identifying these risk factors is important as it will help clinicians identify patients who are at high risk of mortality at an early stage. Through early identification, interventions can be done to reduce the incidence of in-hospital mortality among sepsis patients.

YAPPIS-Finder: A novel method for protein-protein interaction site predictions.

We describe a multi parametric-approach, YAPPIS-Finder, for predicting the PPI sites on protein surface. A non-redundant database of comprised of 2,265 protein-protein interaction interfaces (PPIIs) involving 4,530 protein-protein interacting partners (PPIPs) and depicting the interaction between protein-chains of experimentally determined PPCs was used in designing the YAPPIS-Finder. Parametric score obtained on analyzing these 4,530 PPIPs with respect to their residue interface propensity, their hydrophobic content, and amount of solvation free energy associated with them provided the basis of YAPPIS-Finder. By applying YAPPIS-Finder on another dataset 4,290 PPIPs from 2,145 PPIIs, the optimal range of the parametric scores and protein-probe van der Waals energy of interaction was determined. Subsequently, taking the optimal range of PPIP parametric scores and threshold for protein-probe van der Waals energy of interaction into the consideration, the YAPPIS-Finder was tested on a blind dataset of 554 protein-chains and it was found predicting 69.67% sites correctly. On predicting only one PPI site on each protein-chain, the YAPPIS-Finder found covering 22.91% of actually sites in the predicted site. Contrary to this, the sites predicted by SPPIDER covered 22.7% of actual sites. However, on predicting two PPI sites for each protein-chain, the percentage coverage of actual sites in the predicted sites by YAPPIS-Finder exceeded two-fold (i.e. 41.81%), thus making the YAPPIS-Finder a better method.

Geometrical and electro-static determinants of protein-protein interactions.

Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.

A study of mechanistic mapping of novel SNPs to male breast cancer.

Alterations in BRCA2, PALB2, CHEK2, and p53 genes have been identified for their association with male breast cancer in various studies. The incidence of male breast cancer in India is consistent with its global rate. The present study was carried out with an aim to evaluate the genetic alterations in male breast cancer patients from Malwa region of Punjab, India. Four male breast cancer patients belonging to different families were recruited from Guru Gobind Singh Medical College and Hospital, Faridkot, India. A total of 51 genes reported with implications in the pathogenesis of breast cancer were screened using next generation sequencing. Germline variations were found in BRCA1, BRCA2, PMS2, p53, and PALB2 genes, previously reported to be associated with MBC as well as FBC. In addition to these, 13 novel missense alterations were detected in eight genes including STK11, FZR1, PALB2, BRCA2, NF2, BAP1, BARD1, and CHEK2. Impact of these missense alterations on structure and function of protein was also analyzed through molecular dynamics simulation. Structural analysis of these single nucleotide polymorphisms (SNPs) revealed significant impact on the encoded protein functioning.

PPInS: a repository of protein-protein interaction sitesbase.

Protein-Protein Interaction Sitesbase (PPInS), a high-performance database of protein-protein interacting interfaces, is presented. The atomic level information of the molecular interaction happening amongst various protein chains in protein-protein complexes (as reported in the Protein Data Bank [PDB]) together with their evolutionary information in Structural Classification of Proteins (SCOPe release 2.06), is made available in PPInS. Total 32468 PDB files representing X-ray crystallized multimeric protein-protein complexes with structural resolution better than 2.5 Å had been shortlisted to demarcate the protein-protein interaction interfaces (PPIIs). A total of 111857 PPIIs with ~32.24 million atomic contact pairs (ACPs) were generated and made available on a web server for on-site analysis and downloading purpose. All these PPIIs and protein-protein interacting patches (PPIPs) involved in them, were also analyzed in terms of a number of residues contributing in patch formation, their hydrophobic nature, amount of surface area they contributed in binding, and their homo and heterodimeric nature, to describe the diversity of information covered in PPInS. It was observed that 42.37% of total PPIPs were made up of 6-20 interacting residues, 53.08% PPIPs had interface area ≤1000 Å2 in PPII formation, 82.64% PPIPs were reported with hydrophobicity score of ≤10, and 73.26% PPIPs were homologous to each other with the sequence similarity score ranging from 75-100%. A subset "Non-Redundant Database (NRDB)" of the PPInS containing 2265 PPIIs, with over 1.8 million ACPs corresponding to the 1931 protein-protein complexes (PDBs), was also designed by removing structural redundancies at the level of SCOP superfamily (SCOP release 1.75). The web interface of the PPInS ( http://www.cup.edu.in:99/ppins/home.php ) offers an easy-to-navigate, intuitive and user-friendly environment, and can be accessed by providing PDB ID, SCOP superfamily ID, and protein sequence.

Ganoderic acid, lanostanoid triterpene: a key player in apoptosis.

Cancer is a multifactorial disease, causing behavioral and metabolic alterations, leading to uncontrolled cell proliferation with collateral weakening of immune system. Crucial balance between cell proliferation and cell death determines the fate of a cell, which might progress towards survival or apoptosis. Apoptosis is a complex, programmed, and highly regulated process causing dramatic morphological and biochemical perturbations in the cellular machinery. Ganoderma lucidum is a basidiomycetes, polypore mushroom known for its pharmacological properties in cancer. The major bioconstituents in G. lucidum are terpenoids, polysaccharides, and proteins that target cancer-signaling factors like plasma membrane receptors proteins and adapter molecules. Of all constituents, the major terpenoid, i.e. Ganoderic acid is reported to interact with membrane receptors mainly, receptor tyrosine kinase (RTKs). Ganoderic acid interacts and modulates the signaling network in IR, IGFR-1, IGFR-2, VEGFR-1, VEFGR-2, and EGFR in cancer signaling pathways. It primarily targets NF-κB, RAS-MAPK, PI3K/Akt/mTOR, and cell cycle resulting in apoptosis. This review highlights the role of ganoderic acid in apoptosis and modulations of various signaling proteins in cancer.