Standard Identification Research Articles

Detection of Sars-Cov-2 Nucleic Acid by Rt-Pcr in Symptomatic and Asymptomatic Patients in Dhaka, Bangladesh

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the underlying etiology of COVID-19, a contagious respiratory disease. Proper diagnosis is required to prevent infection from spreading among persons and communities. Precise viral detection is a prerequisite to control the COVID-19 pandemic. Thus, the objective of this study is the detection of SARS-CoV-2 nucleic acid by Real-Time Reverse Transcription-Polymerase Chain Reaction (Real-Time RT-PCR) method of the suspected symptomatic and asymptomatic individuals in Dhaka, Bangladesh. This research was performed in different local hospitals in Dhaka, Bangladesh from June 2021 to January 2022. Samples from suspected individuals were collected following WHO guidelines. Then the samples were placed into a Viral Transport Medium (3 ml sterile VTM). After vortexing, Sansure Biotech Inc. virus nucleic acid RT-PCR test kits were used for diagnosis. Real-Time RT-PCR method was used to identify the positive and negative results of the samples. A total number of 416 COVID-19-suspected individuals were enrolled in this study; 297 (71%) tested real-time RT-PCR-positive, and 119 (29%) were negative. The test results of patients with COVID-19 symptoms were collected and analyzed. Among the positive samples, the positive rate of male patients was higher than female patients. Between age groups, 20 – 59 has shown the highest positivity rate and the lowest was observed below age group 10. Age distribution and clinical characteristics were compared with the neighboring country’s COVID-19 outbreak survey and found to be quite similar. Real-time RT-PCR for SARS-CoV-2 remains the gold standard identification method to verify COVID-19 cases unless and until the World Health Organization (WHO) recommends any alternative approach. Early detection at the primary level of SARS-CoV-2 may aid the clinicians in treatment of COVID-19. All the necessary guidelines need to be followed even after the post-COVID-19 situation. Stam. J. Microbiol. 2024;14(1):13-17

Reliability of wing morphometrics for species identification of human-biting black flies (Diptera: Simuliidae) in Thailand

BackgroundFast and reliable species identification of black flies is essential for research proposes and effective vector control. Besides traditional identification based on morphology, which is usually supplemented with molecular methods, geometric morphometrics (GM) has emerged as a promising tool for identification. Despite its potential, no specific GM techniques have been established for the identification of black fly species.MethodsAdult female black flies collected using human bait, as well as those reared from pupae, were used in this study. Here, landmark-based GM analysis of wings was assessed for the first time to identify human-biting black fly species in Thailand, comparing this approach with the standard morphological identification method and DNA barcoding based on the mitochondrial cytochrome c oxidase subunit I (COI) gene. To explore genetic relationships between species, maximum likelihood (ML) and neighbor-joining (NJ) phylogenetic trees were built. Additionally, three different methods of species delimitation, i.e., assemble species by automatic partitioning (ASAP), generalized mixed yule coalescent (GMYC), and single Poisson tree processes (PTP), were utilized to identify the morphologically defined species. The effectiveness of a COI barcode in identifying black fly species was further examined through the best match (BM) and best close match (BCM) methods.ResultsSeven black fly species, namely Simulium tenebrosum Takaoka, Srisuka & Saeung, 2018 (complex), S. doipuiense Takaoka & Choochote, 2005 (complex), S. nigrogilvum Summers, 1911, S. nodosum Puri, 1933, S. asakoae Takaoka & Davies, 1995, S. chamlongi Takaoka & Suzuki, 1984, and S. umphangense Takaoka, Srisuka & Saeung, 2017 were morphologically identified. Compared with the standard method, the GM analysis based on wing shape showed high success in separating species, achieving an overall accuracy rate of 88.54%. On the other hand, DNA barcoding surpassed wing GM for species identification with a correct identification rate of 98.57%. Species delimitation analyses confirmed the validity of most nominal species, with an exception for S. tenebrosum complex and S. doipuiense complex, being delimited as a single species. Moreover, the analyses unveiled hidden diversity within S. asakoae, indicating the possible existence of up to four putative species.ConclusionsThis study highlights the potential of wing GM as a promising and reliable complementary tool for species identification of human-biting black flies in Thailand.Graphical

Data-driven based identification of closed-loop errors-in-variables systems: a cascaded case

Identification of the EIV system has attracted much interest due to the potential usage of the EIV system in the engineering sciences and elsewhere. It is a more difficult problem in comparison to the standard identification of a linear system. Based on the basic assumptions, a data-driven approach for the closed-loop identification of the cascaded EIV system is proposed. In the absence of external excitation, identifiability and consistency of model estimation of the plant and the controller are achieved. Without relying on prior knowledge, time delay and model order of the plant and the controller are consistently determined from the respective estimated impulse response coefficients that are estimated by using correlation analysis on two sets of routine operating input/output data. Moreover, the model parameters of the plant and the controller are consistently determined by means of the parameterisation algorithm. The effectiveness of the proposed algorithm is illustrated by numerical examples.

Antibiotic susceptibility testing and molecular characterization based on whole-genome sequencing of Streptococcus pneumoniae isolates from pediatric infections at the National Regional Medical Center of Southwest China during the COVID-19 pandemic

BackgroundStreptococcus pneumoniae is a transmitted respiratory pathogen that causes high morbidity and mortality in children, especially those under 5 years of age. During the implementation of population control measures for COVID-19 in mainland China, the Streptococcus pneumoniae detection rate in pediatric patients decreased. However, with the second wave of the COVID-19 pandemic (2022), the incidence of pneumococcal disease (PD) and even invasive pneumococcal disease (IPD) began to rise again.MethodsThis study was conducted from August 2022 to September 2023 at a national regional medical center based mainly in West China Second University Hospital, Sichuan University. The demographic and clinical characteristics of S. pneumoniae-infected pediatric patients were analyzed. All S. pneumoniae isolates were subjected to standardized clinical sample inoculation, culture, subculture, and identification procedures. Next-generation sequencing and analysis were used to determine serotypes and sequence types (STs) and evaluate antibiotic resistance- and virulence-related genes. Antimicrobial susceptibility was determined in AST dishes via the broth microdilution method.ResultsThe prevalent serotypes in the IPD patients were 14, 6A, and 23F, and the prevalent serotypes in the NIPD patients were 19F and 6A. A significant difference in the proportion of patients with serotype 14 was noted between the two groups. A total of 23 STs were identified and classified into 13 different GPSC lineages, including 4 novel STs (ST18449, ST18451, ST18464 and ST18466) and 1 novel allele (ddl1209). According to the interpretation breakpoints for non-meningitis infections, the resistance/nonsusceptibility rates of invasive isolates were as follows: penicillin (0.0%/8.3%), amoxicillin (0.0%/0.0%), cefotaxime (8.3%/16.6%), ceftriaxone (8.3%/8.3%), and cefepime (0.0%/8.3%). The resistance/nonsusceptibility rates of invasive isolates according to the meningitis breakpoints were as follows: penicillin (100.0%), cefotaxime (16.7%/33.4%), ceftriaxone (8.3%/50.0%), and cefepime (8.3%/66.7%). All the isolates were susceptible to rifampicin, levofloxacin, moxifloxacin, linezolid and vancomycin. In addition, the characteristics of the antibiotic resistance-related genes and virulence genes of serotype 19F were significantly different from those of the other serotypes.ConclusionThese data provide valuable information for understanding pediatric pneumococcal disease during the second outbreak of COVID-19 in Southwest China and will contribute to the prevention and treatment of S. pneumoniae infection.

Evaluation of VP4-VP2 sequencing for molecular typing of human enteroviruses.

Enteroviruses and rhinoviruses are highly diverse, with over 300 identified types. Reverse transcription-polymerase chain reaction (RT-PCR) assays targeting their VP1, VP4, and partial VP2 (VP4-pVP2) genomic regions are used for detection and identification. The VP4-pVP2 region is particularly sensitive to RT-PCR detection, making it efficient for clinical specimen analysis. However, a standard type identification method using this region is lacking. This study aimed to establish such a method by examining the divergence of VP4-pVP2 amino acid sequences between enterovirus and rhinovirus prototypes. Pairwise analysis of 249 types indicated a 95% threshold for enterovirus intra-species identification but not for rhinovirus prototypes. Protein BLAST search analyses of representative enterovirus prototypes, including EV-A71, EV-D68, CVA6, CVA10, CVA16, and polioviruses (PVs), validated the 95% threshold for typing, with a few exceptions such as PV1-PV2 and CVA6-CVA10, as well as some EV-C types. This study proposes a criterion for typing based on VP4-pVP2 amino acids, which can aid in rapid enterovirus diagnosis during routine clinical or environmental surveillance and emergency outbreaks. Our research confirms the reliability of the suggested VP4-pVP2-based threshold for typing and its potential application in laboratory settings.

Physics‐Informed Active Learning With Simultaneous Weak‐Form Latent Space Dynamics Identification

ABSTRACTThe parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high‐dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. To enhance robustness against noise, we incorporate the weak‐form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak‐form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent‐space dynamics of high‐dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced‐order modeling. Furthermore, the greedy physics‐informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time‐dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10 Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7 relative errors. Compared with the high‐fidelity models, WgLaSDI achieves 121 to 1779 speed‐up.

Application of Bicadinane Biomarkers in Oil Spill Fingerprinting Studies Involving Southeast Asian Oils

Bicadinanes (C30H52) are a family of non-hopanoid, pentacyclic triterpane biomarkers whose utility in oil spill fingerprinting has, to our knowledge, not been previously explored. Owing to their derivation from Diptocarpaceae tree (dammar) resin, crude oils containing bicadinanes are geographically and temporally limited to Southeast Asian oils sourced from Oligocene or younger strata. Bicadinane-bearing oils produced from Southeast Asia, however, are shipped worldwide and thereby bicadinanes may be present in oils spilled both within and outside of Southeast Asia. Bicadinanes’ relative or absolute concentrations are readily measured using a standard oil spill identification analytical method (GC/MS-SIM) that routinely targets more conventional biomarkers (triterpanes, steranes, and aromatic steroids). In this study, the absolute concentrations of four bicadinane isomers (W, T, T1 and R) were measured in 42 tarballs collected from shorelines along the southern extent of the Straits of Singapore, an area with very high oil tanker and cargo vessel traffic. The tarballs’ specific origins are unknown but safely presumed to be derived from various unknown oil spills and/or oil seeps in the region. GC/FID was used to classify the tarball oils’ level of biodegradation (Levels 1, 4, and 6). The absolute concentration of total bicadinanes ranged from 7.5 to 208 µg/g and varied widely and independent of the level of biodegradation. The relative concentrations among the four bicadinane isomers (T > W>T1∼R; on average, 56% > 20% > 12% each) also showed no obvious or statistically significant trend with biodegradation for the samples studied. Thus, variations in both total and individual isomer concentrations are attributable to the tarballs’ multiple crude oil sources spilled or seeped in the study area. Diagnostic ratios (DRs) are critical components in oil spill identification protocols. A newly proposed DR of (W + T)/hopane varied widely (0.01 to 0.54) and also independently from the more conventionally used DR of 18α(H)-oleanane/hopane (oleanane/hopane; 0.04 to 0.42). Two newly proposed DRs among the four bicadinane isomers, W/T and T/(T1 + R), also varied among the 42 tarball oils studied (0.27 to 0.45 and 1.4 to 3.2, respectively). The variation in the (W + T)/hopane, oleanane/hopane, W/T and T/(T1 + R) ratios was unrelated to the level of biodegradation for the samples studied and exceeded the statistical criteria used in the CEN (2012) oil spill identification protocol (i.e., 95% repeatability limit) and thereby demonstrates these DRs’ utility in comparing and distinguishing among different crude oils containing bicadinanes – and, of course, between crude oils that do and do not contain bicadinanes. The additional specificity offered by bicadinanes argues they should be included as target analytes in oil spill identification studies throughout Southeast Asia or in other areas where Southeast Asian oils are believed to be present (shipped, handled, or used).

Improvement of a MALDI-TOF database for the reliable identification of Candidozyma auris (formally Candida auris) and related species.

Importance Candidozyma auris, also known as Candida auris, has quickly spread across the world, and prompt identification of C. auris from infected individuals is critical. However, a standard identification method is lacking for the identification of C. auris in clinical and public health laboratories. To make matters worse, its biochemical assimilation profile was found to be similar to that of closely related and even no-related species, leading to frequent misidentification. To improve diagnostics of this and closely related species, we created a database of reference mass spectra resulting in the efficient and correct identification of all Candidozyma species by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Moreover, potential pathogenic species of Candidozyma can be effectively identified by MALDI-TOF MS, and differentiated from non-clinically relevant phylogenetic relatives. Thus, MALDI-TOF MS may help expedite laboratory diagnosis and treatment of C. auris and related species of clinical importance and help the clinician to decide on early treatment.

Artificial intelligence in fracture detection on radiographs: a literature review.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

The impact of alcohol consumption on the quality of life of college students: a study from three Slovak universities

Alcohol consumption can influence an individual's quality of life, with the impact varying based on several factors such as the amount of alcohol consumed, frequency of consumption, individual tolerance, and overall health. Slovakia is among the countries with the highest incidence of liver cirrhosis in the world, with alcohol representing a significant risk factor. Therefore, it is important to identify at-risk groups and the potential impact of various factors. The primary objective of this study is to examine the relationship between alcohol consumption and quality of life among college students in the Slovak Universities. A cross-sectional study was conducted in December 2021 and January 2022. The study included college students with an average age of 22.3 years. Participants were surveyed about their alcohol consumption and quality of life using the standardized Alcohol Use Disorders Identification Test (AUDIT) and EuroQol-5D (EQ-5D) questionnaires. These questionnaires were distributed through online school systems. In the study, 88.7% of the students reported drinking alcohol. A low AUDIT score was recorded for 67.1% of the total participants (2573 students). A medium score was found in 23.3% of the participants (893 students). High scores were identified in 2.4% of the participants (92 students), and very high scores were found in 2.2% of the total participants (86 students). Mild pain was reported in 33.8% of cases, while moderate pain was reported in 5.2% of cases. Mild depression and anxiety were reported in 31.1% of the cases, and moderate depression and anxiety were reported in 13.5% of the cases. There is a statistically significant relationship between AUDIT and EuroQOL variables (p < 0.001). This suggests that the association between alcohol use (measured by AUDIT) and health-related quality of life (measured by EuroQOL) is unlikely to be due to chance. Higher alcohol consumption was associated with a poorer quality of life in Slovak college students. Our results suggest a meaningful connection between alcohol consumption behaviors and overall quality of life, highlighting the potential impact of alcohol use on health outcomes. Further research is warranted to explore the nature and causality of this relationship, which could inform interventions aimed at improving health-related quality of life through alcohol use reduction strategies. The association between higher alcohol consumption and poorer quality of life in Slovak college students raises important questions about causality and the complex interplay between alcohol consumption and one’s overall well-being. The significant relationship between higher alcohol consumption and poorer quality of life suggests the need for targeted public health interventions aimed at reducing alcohol consumption among college students. Programs focusing on awareness and education about the negative impacts of excessive drinking could be implemented to promote healthier lifestyles.

Obstetrics and Gynecology Provider Experience Screening for Harmful Alcohol Use: An Unmet Need for Standardized Screening and Intervention.

Introduction Early identification of at-risk alcohol use is key to preventing complications of alcohol use disorder (AUD). Obstetrics and gynecology (OB/GYN) providers are in a unique position to screen for alcohol use in young female patients who may not otherwise seek traditional primary care. The purpose of this study was to learn how OB/GYN providers screen for and manage harmful alcohol use in their clinical care. Method We surveyed OB/GYN providers in a single health care system regarding how they screen for and manage alcohol use. The results were reported using descriptive statistics. Bivariate analysis to assess the impact of physician characteristics on responses was performed using chi-square testing. Results From the results, it was observed that 64.7% and 60.6% of OB/GYN providers screen for alcohol use most or all of the time during obstetrics and gynecologic visits, respectively. Less than half of providers refer patients with at-risk use for further interventions or treatment (41.0% referred to an addiction specialist, 41.0% recommended discussing their behavior with their PCP, 35.9% referred to social work, 10.4% referred to gastroenterology or hepatology). Providers who screened did so most commonly by directly asking patients about their alcohol use (70.6% during obstetrics and 60.1% during gynecologic visits).Only 11.8% and 15.2% of providers who screened did so using standardized screening methods (e.g. CAGE (Cut down, Annoyed, Guilty, and Eye-opener) questionnaire and Alcohol Use Disorders Identification Test-Consumption (AUDIT-C)) during obstetrics and gynecologic visits, respectively.Only 30.8% of the providers felt they received adequate training on AUD. Providers who had experience taking care of a patient with severe alcohol-related liver disease (100% v. 0%, χ2=4.69, p=0.047) or died from this (100% v. 0%, χ2=11.35, p<0.01) were more likely to refer to gastroenterology. Conclusions Further work needs to be done to improve screening for and management of harmful alcohol use in OB/GYN clinics. Standardized screening methods were rarely used. Education of OB/GYN providers on AUD management and facilitating referrals for addiction care are opportunities to prevent the consequences of AUD in at-risk female patients.

All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soils

While tire wear particles (TWP) have been estimated to represent more than 90% of the total microplastic (MP) emitted in European countries and may have environmental health effects, only few data about TWP concentrations and characteristics are available today. The lack of data stems from the fact that no standardized, cost efficient or accessible extraction and identification method is available yet. We present a method allowing the extraction of TWP from soil, performing analysis with a conventional optical microscope and a machine learning approach to identify TWP in soil based on their colour. The lowest size of TWP which could be measured reliably with an acceptable recovery using our experimental set-up was 35 µm. Further improvements would be possible given more advanced technical infrastructure (higher optical magnification and image quality). Our method showed a mean recovery of 85% in the 35–2000 µm particle size range and no blank contamination. We tested for possible interference from charcoal (as another black soil component with similar properties) in the soils and found a reduction of the interference from charcoal by 92% during extraction. We applied our method to a highway adjacent soil at 1 m, 2 m, 5 m, and 10 m and detected TWP in all samples with a tendency to higher concentrations at 1 m and 2 m from the road compared to 10 m from the road. The observed TWP concentrations were in the same order of magnitude as what was previously reported in literature in highway adjacent soils. These results demonstrate the potential of the method to provide quantitative data on the occurrence and characteristics of TWP in the environment. The method can be easily implemented in many labs, and help to address our knowledge gap regarding TWP concentrations in soils.

Derivation and Validation of the PRECISE-HBR Score to Predict Bleeding After Percutaneous Coronary Intervention.

Accurate bleeding risk stratification after percutaneous coronary intervention (PCI) is important for treatment individualization. However, there is still an unmet need for a more precise and standardized identification of high bleeding risk patients. We derived and validated a novel bleeding risk score by augmenting the PRECISE-DAPT score with the Academic Research Consortium for High Bleeding Risk (ARC-HBR) criteria. The derivation cohort comprised 29,188 patients undergoing PCI, of whom 1136 (3.9%) had a Bleeding Academic Research Consortium (BARC) 3 or 5 bleeding at 1 year, from four contemporary real-world registries and the XIENCE V USA trial. The PRECISE-DAPT score was refitted with a Fine-Gray model in the derivation cohort and extended with the ARC-HBR criteria. The primary outcome was BARC 3 or 5 bleeding within 1 year. Independent predictors of BARC 3 or 5 bleeding were selected at multivariable analysis (p<0.01). The discrimination of the score was internally assessed with apparent validation and cross-validation. The score was externally validated in 4578 patients from the MASTER DAPT trial and 5970 patients from the STOPDAPT-2 total cohort. The PRECISE-HBR score (age, estimated glomerular filtration rate, hemoglobin, white-blood-cell count, previous bleeding, oral anticoagulation, and ARC-HBR criteria) showed an area under the curve (AUC) for 1-year BARC 3 or 5 bleeding of 0.73 (95% CI, 0.71-0.74) at apparent validation, 0.72 (95% CI, 0.70-0.73) at cross-validation, 0.74 (95% CI, 0.68-0.80) in the MASTER DAPT, and 0.73 (95% CI, 0.66-0.79) in the STOPDAPT-2, with superior discrimination than the PRECISE-DAPT (cross-validation: Δ AUC, 0.01; p=0.02; MASTER DAPT: Δ AUC, 0.05; p=0.004; STOPDAPT-2: Δ AUC, 0.02; p=0.20) and other risk scores. In the derivation cohort, a cut-off of 23 points identified 11,414 patients (39.1%) with a 1-year BARC 3 or 5 bleeding risk ≥4%. An alternative version of the score, including acute myocardial infarction on admission instead of white-blood-cell count, showed similar predictive ability. The PRECISE-HBR score is a contemporary, simple 7-item risk score to predict bleeding after PCI, offering a moderate improvement in discrimination over multiple existing scores. Further evaluation is required to assess its impact on clinical practice.

High-throughput solutions in tumor organoids: From culture to drug screening.

Tumor organoids have emerged as an ideal in vitro model for patient-derived tissues, as they recapitulate the characteristics of the source tumor tissue to a certain extent, offering the potential for personalized tumor therapy and demonstrating significant promise in pharmaceutical research and development. However, establishing and applying this model involves multiple labor-intensive and time-consuming experimental steps and lacks standardized protocols and uniform identification criteria. Thus, high-throughput solutions are essential for the widespread adoption of tumor organoid models. This review provides a comprehensive overview of current high-throughput solutions across the entire workflow of tumor organoids, from sampling and culture to drug screening. Furthermore, we explore various technologies that can control and optimize single-cell preparation, organoid culture, and drug screening with the ultimate goal of ensuring the automation and high efficiency of the culture system and identifying more effective tumor therapeutics.

Palliative Care Referral Patterns and Implications for Standardization in Cardiac ICU.

Evidence suggests that pediatric palliative care involvement (PPC) is beneficial to medically complex patients. Historically, PPC involvement has been overlooked or delayed and varies by institution but PPC awareness has increased in cardiovascular ICUs (CVICU) and so we investigated frequency and timeliness of PPC referral for patients dying in ICU. Retrospective study of pediatric cardiac patients experiencing death in ICU to review PPC presence and timing of initial PPC, most recent PPC, and interventions, therapies, CPR, and presence of do-not-resuscitate DNR discussion. Fifty-four patients died during a 5-year period aged 11d-17y (54% male). PPC involvement occurred in 40/54 (74%). Of those patients without PPC, the Center to Advance Palliative Care (CAPC) guidelines would have supported PPC in 11/14 (79%). DNR discussion was more likely in PPC patients (63% vs 14%; p = 0.0011), though often only on DOD. Comparing prior to DOD, PPC patients were still more likely to have DNR discussion (55% vs 0%; p = 0.0003). PPC patients were no less likely to have CPR on DOD (28% vs 43%, p = 0.29). PPC occurred frequently in patients experiencing death in CVICU. However, frequently the initial PPC occurred within a week or day of death. Patients without PPC would often qualify under published guidelines. Standardization, timing, and patient identification for PPC will expand efficacy in CVICU.

Clinical significance of serum soluble scavenger receptor CD163 in patients with lupus nephritis.

The soluble CD163 (sCD163) was elevated in systemic lupus erythematosus (SLE) patients. To study whether serum sCD163 could be used to predict the occurrence and prognosis of lupus nephritis (LN). The recruited patients were classified into different groups according to standard identification criteria. The patients with LN. 11 indices were analyzed and compared in SLE and LN patients. Furthermore, the level of serum sCD163 was detected using an enzyme-linked immunosorbent assay. Meanwhile, the receiver operating characteristic analysis was performed to evaluate the prediction effect of sCD163. Additionally, spearman correlation analysis of serum sCD163 with indices was conducted. There were six positive indices and one negative risk factor correlated to LN. sCD163 was elevated in LN patients and could be used to diagnose LN. Importantly, sCD163 was increased in LN patients with a heavy SLE disease activity index. Finally, it was revealed that the level of sCD163 was higher in the LN patients with no response than that with complete or partial response, which also could predict the prognosis of LN. Serum sCD163 was elevated in LN patients than in SLE patients, which could be used to predict the occurrence and prognosis of LN.

Crafting an ultrashort workplace incivility scale and determining cutoffs for varied risk levels through item response theory

Workplace incivility (WI), characterized by disrespectful or rude behavior in the workplace, is linked to poor mental health and overall well-being. The Workplace Incivility Scale (WIS) is a popular 7-item measure for assessing WI. There is a current need for an ultrashort conceptually clear version of the WIS. In addition, the cutoffs for varied at-risks of WI, requiring intervention, remains unknown. Using data from 426 employees across diverse organizations, we employed item response theory (IRT) to create an ultrashort WIS and establish cutoffs for high, moderate, low, and at-not risk of WI. Confirmatory factor analyses were utilized to validate WIS construct validity. In supporting convergent validity, WIS was correlated with workplace bullying and poor health-related quality of life. All items demonstrated adequate severity threshold parameters with very high discrimination and good reliability parameters except for item WIS7. We successfully developed an ultrashort and valid 3-item WIS, specifically comprising items WIS2–4, and identified cutoffs for varying levels of WI risk. This streamlined measure aims to reduce response burden and foster a healthier organizational culture. In essence, cutoff points streamline the classification process, allowing for quicker and more standardized identification of at-risk employees. This concise, valid, and reliable ultrashort WIS holds potential for use in intervention studies conducted by organizational and occupational health psychologists, ultimately promoting employee well-being and cultivating a positive workplace environment. The current study further advances the existing theoretical framework rooted in the social interactionist perspective by delineating WI as a distinct and independent construct.

The role of economics and the quality of antitrust case assessment in China: an empirical investigation

This article empirically measures indicators capturing the role of economics in the antitrust decisions of the Competition Authorities of China from 2010 – 2021. The methodology allows that identification of the legal standards (LSs) adopted in assessing different conduct types and their evolution. The LSs are compared to their theoretically optimal level in order to deduce quality of enforcement. Comparative analysis is undertaken with published results on the role of economics for EC’s DGCOMP, UK’s CMA and Russia’s FAS. The Chinese Authorities’ enforcement record lags behind in quality that of DGCOMP and CMA in abuse of dominance cases though it is already well ahead of countries in which their modern Competition Law enforcement started at about the same time (2008), like Russia.

Enhanced Malware Detection Using Grey Wolf Optimization and Deep Belief Neural Networks

Standard identification methods are flattering and less effective as attacks from malware get increasingly sophisticated. Considering current malware outbreaks employ tactics such as polymorphism, obfuscation and encryption, to avert identification, growing complicated approaches must be developed. This paper deals with a mixed model utilizing Deep Belief Neural Network (DBNN) for classifying and Grey Wolf Optimization (GWO) for choosing features. Whereas DBNN encodes complicated patterns by hierarchical learning, GWO optimizes the choosing of the more essential features, lowering the cost of computing and dataset complexity. Investigations reveal that the suggested GWO-DBNN model beats existing machine learning procedures in terms of detection accuracy, recall, precision, and false positive rate (FPR). These mixed tactics offer dependable and scalable solutions to the challenges faced by modern malware threats.

Enhanced Malware Detection Using Grey Wolf Optimization and Deep Belief Neural Networks

Standard identification methods are flattering and less effective as attacks from malware get increasingly sophisticated. Considering current malware outbreaks employ tactics such as polymorphism, obfuscation and encryption, to avert identification, growing complicated approaches must be developed. This paper deals with a mixed model utilizing Deep Belief Neural Network (DBNN) for classifying and Grey Wolf Optimization (GWO) for choosing features. Whereas DBNN encodes complicated patterns by hierarchical learning, GWO optimizes the choosing of the more essential features, lowering the cost of computing and dataset complexity. Investigations reveal that the suggested GWO-DBNN model beats existing machine learning procedures in terms of detection accuracy, recall, precision, and false positive rate (FPR). These mixed tactics offer dependable and scalable solutions to the challenges faced by modern malware threats.