Database Search Methods Research Articles

Spiritual Support for Colorectal Cancer Patients to Improve Their Quality of Life: A Literature Review

Intoduction: Spiritual assistance is an important action for colorectal cancer patients to improve their quality of life. Methods: The database search method uses systematic and comprehensive electronic data from four databases, namely: Scopus, CINAHL, PubMed, and Proquest and Manual. Search terms "OR", spirituality "AND", spiritual support "AND" quality of life. Article search using the PICO model (population, intervention, comparison, outcome). Data selection is carried out by determining inclusion and exclusion criteria, carried out after identifying the problem, the group to be studied, the intervention to be provided, and the results to be measured. A total of 25 articles were discoered. Results: The spiritual interventions provided are mindfulness, meditation, music therapy and telephone counseling. This is intended to motivate patients to be more enthusiastic about undergoing treatment and improve their quality of life. Conclusions: Spiritual support influences the quality of life of colorectal cancer patients, namely providing a sense of peace and the desire to continue undergoing the treatment process, with routine daily activities as evidence of improving the patient's quality of life.

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications.

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry.

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.'s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

Research on structural modulus inversion method of asphalt pavement based on BP neural networ

The current inversion methods based on pattern recognition method, database search method, genetic algorithm and other inversion methods are difficult to solve the absolute convergence problem of structural modulus inversion for asphalt pavements with more than three layers, while the deep learning models and methods widely used in the scenarios of image recognition, speech recognition, etc. as well as the ways to implement them have been increasingly improved, and they can be applied to solve the problem of structural modulus inversion for asphalt pavements. This study aims to solve the modulus inversion problem of multi-layer asphalt pavement structures, obtaining enough theoretical bending basin data of asphalt pavement structures as training samples through mechanical theory and programming calculations, and using the BP neural network model to train the prediction model of structural layer modulus inversion. The test results show that the BP neural network inverse asphalt pavement structural modulus model established in this paper can not only get the prediction results quickly and effectively, but also the prediction results have high accuracy, which provides an effective way for solving the modulus inversion problem of asphalt pavement structure with more than three layers by using the BP neural network to solve the pain point and bottleneck problems in the industry.

Tracheostomy and long-term invasive ventilation decision-making in children: A scoping review.

An increasing number of children are surviving critical illnesses requiring tracheostomy/long-term ventilation (LTV). This scoping review seeks to collate the available evidence on decision-making for tracheostomy/LTV in children. Systematic searches of electronic databases and websites were conducted for articles and reports. Inclusion criteria included: (1) children 0-18 years old; (2) described use of tracheostomy or tracheostomy/LTV; and (3) information on recommendations for tracheostomy decision-making or decision-making experiences of family-caregivers or health care providers. Articles not written in English were excluded. Of the 4463 records identified through database search and other methods, a total of 84 articles, 2 dissertations, 1 book chapter, 3 consensus statement/society guidelines, and 8 pieces of grey literature were included. Main thematic domains identified were: (1) legal and moral standards for decision-making; (2) decision-making models, roles of decision-makers, and decisional aids towards a shared decision-making model; (3) experiences and perspectives of decision-makers; (4) health system and society considerations; and (5) conflict resolution and legal considerations. A high degree of uncertainty and complexity is involved in tracheostomy/LTV decision-making. There is a need for a standardized decision-support process that is consistent with a child's best interests and shared decision-making. Strategies for optimizing communication and mechanism for managing disputes are needed.

Comparative Clinical Assessment and Risk Stratification of COVID-19 and Influenza Infections in Adults and Children: A Comprehensive Systematic Review and Meta-Analysis.

This study aimed to compare the clinical risks and outcomes of COVID-19 and influenza. The search for relevant articles was conducted using both a database search method and a manual search, which involved searching through the reference lists of articles related to the topic for additional studies. The Quality assessment was carried out using the Newcastle Ottawa tool, and the data analysis was conducted using the Review Manager Software (RevMan 5.4.1). The meta-analysis results indicated that COVID-19 patients had similar lengths of hospital stays (SMD: -0.25; 95% CI: -0.60-0.11; p=0.17). However, COVID-19 patients had significantly higher mortality rates (RR: 0.28; 95% CI: 0.21-0.37; p<0.0001), in-hospital complications (RR: 0.57; 95% CI: 0.50-0.65; p<0.00001), intensive care unit (ICU) admissions (OR: 0.48; 95% CI: 0.37-0.61; p<0.00001), length of ICU stay (SMD: -0.45; 95% CI: -0.83-0.06; p=0.02), and mechanical ventilation use (OR: 0.36; 95% CI: 0.28-0.46; p<0.00001). The findings suggested that COVID-19 was more severe than influenza. Therefore, "flu-like" symptoms should not be dismissed without a clear diagnosis, especially during the winter when influenza is more prevalent.

Exploring the Mental Health Challenges of Emergency Medicine and Critical Care Professionals: A Comprehensive Review and Meta-Analysis.

Burnout and depression are global problems affecting healthcare providers, especially those working in stressful departments such as emergency departments (EDs) and critical care units (CCUs). However, pooled data analysis comparing healthcare providers operating in the ED and CCU is yet to be conducted. Therefore, this meta-analysis was systematically conducted to investigate and compare the prevalence of burnout and depression among emergency medicine (EM) and critical care medicine (CCM) professionals. We systematically searched for articles related to our research topic using the database search method and manual search method, which involved reviewing the reference lists of articles from electronic databases for additional studies. After screening the literature from the databases using the eligibility criteria, a quality appraisal using the Newcastle-Ottawa scale was performed on the eligible studies. In addition, a meta-analysis using the Review Manager software was performed to investigate the prevalence rates of burnout and depression. A total of 10 studies with 1,353 EM and 1,250 CCM professionals were included for analysis in the present study. The pooled analysis did not establish any considerable differences between EM and CCM healthcare workers on the prevalence of high emotional exhaustion (EE) (odds ratio (OR) = 1.01; 95% confidence interval (CI) = 0.46-2.19; p = 0.98), high depersonalization (OR = 1.16; 95% CI = 0.61-2.21; p = 0.64), low personal accomplishment (PA) (OR = 0.87; 95% CI = 0.67 - 1.12; p = 0.28), and depression (OR = 1.20; 95% CI = 0.74-1.95; p = 0.45). Moreover, pooled data showed no considerable differences in EE scores (mean difference (MD) = -1.07; 95% CI = -4.24-2.09; p = 0.51) and depersonalization scores (MD = -0.31; 95% CI = -1.35-0.73; p = 0.56). However, EM healthcare workers seemed to have considerably lower PA scores than their CCM counterparts (MD = 0.12; 95% CI = 0.08-0.16; p < 0.00001). No considerable difference was recorded in the prevalence of burnout and depression between EM and CCM healthcare workers. However, our findings suggest that EM professionals have lower PA scores than CCM professionals; therefore, more attention should be paid to the mental health of EM professionals to improve their PA.

ProbRank: An efficient DNA database search method for complex mixtures per a quantitative likelihood ratio model

Searching a DNA Database with a DNA profile from an evidentiary trace can provide investigative leads in a forensic case. Various searching approaches exist such as conventional methods based on matching alleles or more advanced methods computing likelihood ratios (LR) while considering drop-in and drop-out. Here we examine the potential of using a quantitative LR model (EuroForMix model incorporated in ProbRank method) that takes peak heights into account in comparison to a qualitative LR model (LRmix model implemented in SmartRank method). Both methods present DNA database candidates in order of decreasing LR. Especially regarding minor contributors in complex mixtures, the method using the quantitative model outperforms the method using the qualitative model in terms of sensitivity and specificity as more true donors and less adventitious matches are retrieved. ProbRank is to be implemented in DNAStatistX and is sufficiently fast for daily use.

Identification of 113 new histone marks by CHiMA, a tailored database search strategy.

Shotgun proteomics has been widely used to identify histone marks. Conventional database search methods rely on the "target-decoy" strategy to calculate the false discovery rate (FDR) and distinguish true peptide-spectrum matches (PSMs) from false ones. This strategy has a caveat of inaccurate FDR caused by the small data size of histone marks. To address this challenge, we developed a tailored database search strategy, named "Comprehensive Histone Mark Analysis (CHiMA)." Instead of target-decoy-based FDR, this method uses "50% matched fragment ions" as the key criterion to identify high-confidence PSMs. CHiMA identified twice as many histone modification sites as the conventional method in benchmark datasets. Reanalysis of our previous proteomics data using CHiMA led to the identification of 113 new histone marks for four types of lysine acylations, almost doubling the number of previously reported marks. This tool not only offers a valuable approach for identifying histone modifications but also greatly expands the repertoire of histone marks.

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

NPvis: An Interactive Visualizer of Peptidic Natural Product-MS/MS Matches.

Peptidic natural products (PNPs) represent a medically important class of secondary metabolites that includes antibiotics, anti-inflammatory and antitumor agents. Advances in tandem mass spectra (MS/MS) acquisition and in silico database search methods have enabled high-throughput PNP discovery. However, the resulting spectra annotations are often error-prone and their validation remains a bottleneck. Here, we present NPvis, a visualizer suitable for the evaluation of PNP–MS/MS matches. The tool interactively maps annotated spectrum peaks to the corresponding PNP fragments and allows researchers to assess the match correctness. NPvis accounts for the wide chemical diversity of PNPs that prevents the use of the existing proteomics visualizers. Moreover, NPvis works even if the exact chemical structure of the matching PNP is unknown. The tool is available online and as a standalone application. We hope that it will benefit the community by streamlining PNP data analysis and validation.

Content recruiting: Obstacle to institutional repository development in university libraries in Nigeria and South Africa

Purpose: The purpose of this study was to bring to light the obstacles to collecting contents from faculty members in universities in Nigeria and South Africa. Design/methodology/approach: The study adopted a survey research design. OpenDOAR database search, online questionnaire, and IRs investigation methods were used to collect data from 41 institutional repositories in universities in Nigeria and South Africa. Findings: The study found that the most popular content submitted to IRs in Nigeria and South Africa are electronic theses and dissertations, journal articles, special items (Africana collections, images, artifacts, paintings), conference and workshop papers, and research and technical reports. The comparison of institutional repository development between the two countries revealed that, South Africa is far ahead of Nigeria in terms of institutional repository development using contents emanating from the universities. South Africa is a leading African country in terms of Open Access (OA) policies and South Africa embraced open access movement earlier than Nigeria. The study identified some factors such as: lack of awareness of the existence of institutional repositories by faculty members; lack of mandatory self-archiving policy by institutions or funding bodies; considering copyright and intellectual property issues; some faculty members are skeptical on the principle of open access; some faculty members are not interested in IR; some faculty members believe it is too risky to submit their research work to IR; lack of technical skills for some faculty members to self-archive as obstacles which are peculiar to collecting contents from various contributors in both counties. Research Implication: The findings will inform University Librarians, University management and policy makers on possible ways to overcome the obstacles to recruiting content to IRs. Originality/value: The study reported on the obstacles of recruiting content from faculty members in universities in Nigeria and South Africa and the findings will inform stakeholders by designing strategies to overcome such obstacles and populate the IRs with different contents.

The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms

Proteomics aims to characterise system-wide protein expression and typically relies on mass-spectrometry and peptide fragmentation, followed by a database search for protein identification. It has wide ranging applications from clinical to environmental settings and virtually impacts on every area of biology. In that context, de novo peptide sequencing is becoming increasingly popular. Historically its performance lagged behind database search methods but with the integration of machine learning, this field of research is gaining momentum. To enable de novo peptide sequencing to realise its full potential, it is critical to explore the mass spectrometry data underpinning peptide identification. In this research we investigate the characteristics of tandem mass spectra using 8 published datasets. We then evaluate two state of the art de novo peptide sequencing algorithms, Novor and DeepNovo, with a particular focus on their performance with regard to missing fragmentation cleavage sites and noise. DeepNovo was found to perform better than Novor overall. However, Novor recalled more correct amino acids when 6 or more cleavage sites were missing. Furthermore, less than 11% of each algorithms’ correct peptide predictions emanate from data with more than one missing cleavage site, highlighting the issues missing cleavages pose. We further investigate how the algorithms manage to correctly identify peptides with many of these missing fragmentation cleavages. We show how noise negatively impacts the performance of both algorithms, when high intensity peaks are considered. Finally, we provide recommendations regarding further algorithms’ improvements and offer potential avenues to overcome current inherent data limitations.

De-sialylation of glycopeptides by acid treatment: enhancing sialic acid removal without reducing the identification.

Sialic acid, a common terminal monosaccharide on many glycoconjugates, plays essential roles in many biological processes such as immune responses, pathogen recognition, and cancer development. For various purposes, sialic acids may need to be removed from glycopeptides or glycans, mainly using enzymatical or chemical approaches. In this study, we found that most commonly used chemical methods couldn't completely remove sialic acids from glycopeptides. Although the de-sialylation efficiency could be further enhanced by increasing the treatment time or acid concentration, the undesirable side reactions on the peptide portion would decrease glycopeptide identification. By adding the deamidation on carbamidomethyl-cysteine (C), asparagine (N), and glutamine (Q) residues as a variable modification during database search, most of the unidentified spectra could be recovered. This optional acid-treatment and database search method for the complete removal of sialic acids without losing much spectral identification should be quite useful for many glycomic and glycoproteomic studies.

Pengaruh Terapi Okupasi Aktivitas Waktu Luang (Menyapu, Membersihkan Tempat Tidur, Menanam Tanaman dan Menggambar) terhadap Gejala Halusinasi Pendengaran

The prevalence of severe mental disorders such as schizophrenia reaches about 400,000 people or as much as 1.7 per 1,000 population. The most common hallucinations are auditory hallucinations, about 70% of patients experience auditory hallucinations, 20% visual hallucinations, and 10% are olfactory, tasting and tactile hallucinations. One of the treatments for hallucinatory patients is occupational therapy. Occupational therapy is a very important way or form of supportive psychotherapy to improve patient recovery. Occupational therapy helps stimulate the patient through activities he enjoys. One type of occupational therapy that is indicated for patients who hallucinate is free time activities such as sweeping, cleaning the bed. The purpose of writing this literature review was to determine the effect of occupational therapy on symptoms in patients with auditory hallucinations based on a literature review search. The design in this scientific paper was a literature review using an electronic database search method and a google schoolar search source. From this writing, found 3 articles on Google Schoolar that discussed the effect of occupational therapy on leisure activities based on classification and outcomes in accordance with the research criteria. The results of the literature review of the three articles show that occupational therapy for leisure activities has an effect on changes in symptoms in patients with auditory hallucinations. Based on these results, occupational activity therapy can be applied to patients with auditory hallucinations to reduce their symptoms.

Pengaruh Range Of Motion (ROM) Angkle Terhadap Resiko Deep Vein Thrombosis (DVT) Pada Pasien Diabetes Mellitus Dengan Luka Ulkus: Literature Review

Background: Diabetes Mellitus (DM) is the most chronic disease throughout the world which is a chronic metabolic disease due to pancreas that cannot produce enough insulin and the body cannot use effective insulin, as evidenced by the number of diabetes mellitus prevalence that can continue at any time increased. Diabetes mellitus is a metabolic disease which is very high risk of death. Patients with diabetes mellitus participate with thrombosis in blood vessels, one of which is diabetic foot ulcer. Thrombosis in patients with diabetic foot ulcers will increase thrombosis in the lower limbs that are part of venous thromboembolism, or Deep Vein Thrombosis (DVT). One effort to repair the wound can be done with practice. Exercise is one of the first management strategies proposed for new patients diagnosed with diabetes, because along with diet and policy modifications, exercise and exercise are important components of all diabetes intervention programs, which by doing exercises can help very much.Research Objectives: This literature review discusses the causes of deep venous thrombosis (DVT), causes of ulcer wounds and looks for the influence of the Angkle range of motion (ROM) on the risk of Deep Vein Thromosis (DVT) in patients with diabetes mellitus with ulcer wounds.Method: The literature database search method was conducted at Google Scholar and Pubmed. The literature used is literature that meets the inclusion criteria and published the last five years, is approved then selected and reviewed.Results: Final results There are 4 literatures that can be reviewed. Based on the literature conclusions, it can be found several exercises in patients with diabetes mellitus so as to increase diabetes, and can reduce the need for Deep Vein Thromosis (DVT). To find out the risk of Deep Vein thrombosis (DVT) can be determined by the D-Dimer test. If the D-Dimer test shows positive results, then the patient must use DVT

Evaluasi Penggunaan Surgical Safety Checklist terhadap Kematian Pasien setelah Laparotomi Darurat di Kamar Operasi

This study aims to analyze articles about surgical safety checklists on patient deaths after emergency surgery in the operating room, especially in Indonesia. The literature database search method was carried out on Google Scholar and Pubmed. The literature used is literature that meets the inclusion criteria and was published in the last five years. The results showed that the use of the surgical safety checklist is highly expected to be applied to preoperative and postoperative procedures that reduce surgical complications and determine the evaluation of the use of the surgical safety checklist on patient mortality after emergency laparotomy in the operating room. In conclusion, the application of a surgical safety checklist in emergency laparotomy surgery will be very helpful in reducing the occurrence of errors in operating procedures and complications of emergency laparatomy operations.&#x0D; &#x0D; Keywords: Patient mortality, Emergency Laparotomy, Surgical Safety Checklist

MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data

BackgroundA few recent large efforts significantly expanded the collection of human-associated bacterial genomes, which now contains thousands of entities including reference complete/draft genomes and metagenome assembled genomes (MAGs). These genomes provide useful resource for studying the functionality of the human-associated microbiome and their relationship with human health and diseases. One application of these genomes is to provide a universal reference for database search in metaproteomic studies, when matched metagenomic/metatranscriptomic data are unavailable. However, a greater collection of reference genomes may not necessarily result in better peptide/protein identification because the increase of search space often leads to fewer spectrum-peptide matches, not to mention the drastic increase of computation time.-eZXE1L8R6JnTVoLWAe_NTVideo MethodsHere, we present a new approach that uses two steps to optimize the use of the reference genomes and MAGs as the universal reference for human gut metaproteomic MS/MS data analysis. The first step is to use only the high-abundance proteins (HAPs) (i.e., ribosomal proteins and elongation factors) for metaproteomic MS/MS database search and, based on the identification results, to derive the taxonomic composition of the underlying microbial community. The second step is to expand the search database by including all proteins from identified abundant species. We call our approach HAPiID (HAPs guided metaproteomics IDentification).ResultsWe tested our approach using human gut metaproteomic datasets from a previous study and compared it to the state-of-the-art reference database search method MetaPro-IQ for metaproteomic identification in studying human gut microbiota. Our results show that our two-steps method not only performed significantly faster but also was able to identify more peptides. We further demonstrated the application of HAPiID to revealing protein profiles of individual human-associated bacterial species, one or a few species at a time, using metaproteomic data.ConclusionsThe HAP guided profiling approach presents a novel effective way for constructing target database for metaproteomic data analysis. The HAPiID pipeline built upon this approach provides a universal tool for analyzing human gut-associated metaproteomic data.

FPTMS: Frequency-based approach to identify the peptide from the low-energy collision-induced dissociation tandem mass spectra

The database search method is a widely accepted method to assign a peptide to the tandem mass spectra. In this study, a new flexible method- FPTMS is introduced to interpret the tandem mass spectra with the known peptide sequences in a protein database. Here the frequency of occurrence of fragment ion peaks extracted from the extensive spectral library is used to predict the theoretical tandem mass spectra of the peptides. The dot product scoring and windowed-xcorr scoring methods were implemented to score the experimental spectrum against the theoretical peptide spectra. Windowed-xcorr is introduced to tackle the mass errors and the cleavage position of the fragmentation process. The new method with windowed-xcorr shows an improved identification rate compared to the existing search engines Crux-Tide and X!Tandem at 1% False Discovery Rate (FDR) for the dataset considered in this study. SignificanceIdentifying or sequencing of the peptide from tandem mass spectra is an important application in mass spectrometry-based proteomics. Collision-induced dissociation (CID) fragmentation spectra have been widely used to develop a peptide identification algorithm using database search strategy. CID fragmentation behavior is a complex process and found to have dependency on the sequences of peptide, charge state, and residue content. The inclusion of more features of peptide fragmentation behavior and adaptable scoring algorithm improves the efficiency of the peptide identification algorithm.