Garg incontinence scores: New scoring system on the horizon to evaluate fecal incontinence. Will it make a difference?

The main aim of this opinion review is to comment on the recent article published by Garg et al in the World Journal of Gastroenterology 2023;29:4593–4603.The authors in the published article developed a new scoring system,Garg incon-tinence scores(GIS),for fecal incontinence(FI).FI is a chronic debilitating disease that has a severe negative impact on the quality of life of the patients.Rome IV criteria define FI as multiple episodes of solid or liquid stool passed into the clothes at least twice a month.The associated social stigmatization often leads to significant under-reporting of the condition,which further impairs management.An important point is that the complexity and vagueness of the disease make it difficult for the patients to properly define and report the magnitude of the problem to their physicians.Due to this,the management becomes even more difficult.This issue is resolved up to a considerable extent by a scoring ques-tionnaire.There were several scoring systems in use for the last three decades.The prominent of them were the Cleveland Clinic scoring system or the Wexner scoring system,St.Marks Hospital or Vaizey’s scores,and the FI severity index.However,there were several shortcomings in these scoring systems.In the opinion review,we tried to analyze the strength of GIS and compare it to the existing scoring systems.The main pitfalls in the existing scoring systems were that most of them gave equal weightage to different types of FI(solid,liquid,flatus,etc.),were not comprehensive,and took only the surgeon’s perception of FI into view.In GIS,almost all shortcomings of previous scoring systems had been addressed:different weights were assigned to different types of FI by a robust statistical methodology;the scoring system was made comprehensive by including all types of FI that were previously omitted(urge,stress and mucus FI)and gave priority to patients’rather than the physicians’perceptions while developing the scoring system.Due to this,GIS indeed looked like a paradigm shift in the evaluation of FI.However,it is too early to conclude this,as GIS needs to be validated for accuracy and simplicity in future studies.

Development of a prognostic scoring system for hepatic vena cava Budd-Chiari syndrome with hepatocellular carcinoma

Background:Hepatocellular carcinoma(HCC)is a serious complication of hepatic vena cava Budd-Chiari syndrome(HVC-BCS)that significantly reduces the survival time of patients.Our study aimed to analyze the prognostic factors influencing the survival of HVC-BCS patients with HCC and to develop a prognostic scoring system.Methods:The clinical and follow-up data of 64 HVC-BCS patients with HCC who received invasive treatment at the First Affiliated Hospital of Zhengzhou University between January 2015 and December 2019 were retrospectively analyzed.Kaplan-Meier curves and log-rank tests were used to analyze the survival curve of patients and the difference in prognoses between the groups.Univariate and multivariate Cox regression analyses were performed to analyze the influence of biochemical,tumor,and etiological characteristics on the total survival time of patients,and a new prognostic scoring system was developed according to the regression coefficients of the independent predictors in the statistical model.The prediction efficiency was evaluated using the time-dependent receiver operating characteristics curve and concordance index.Results:Multivariate analysis showed that serum albumin level<34 g/L[hazard ratio(HR)=4.207,95%confidence interval(CI):1.816-8.932,P=0.001],maximum tumor diameter>7 cm(HR=8.623,95%CI:3.771-19.715,P<0.001),and inferior vena cava stenosis(HR=3.612,95%CI:1.646-7.928,P=0.001)were independent predictors of survival.A prognostic scoring system was developed according to the above-mentioned independent predictors,and patients were classified into grades A,B,C and D.Significant differences in survival were found among the four groups.Conclusions:This study successfully developed a prognostic scoring system for HVC-BCS patients with HCC,which is helpful for clinical evaluation of patient prognosis.

Diagnostic tools for fecal incontinence: Scoring systems are the crucial first step

The main aim of this editorial is to comment on the recent article published by Garg et al in the World Journal of Gastroenterology 2023;29:4593-4603.This original research presents a new scoring system for fecal incontinence.Fecal incontinence is a chronic disease with a severe impact on the quality of life of the patients.Substantial social stigmatization often leads to significant underreporting of the condition even during visits to a specialist and could lead to further misman-agement or non-existent management of the disease.An important fact is that patients are often unable to describe their condition when not asked precisely defined questions.This problem is partially resolved by scoring questionnaires.Several scoring systems are commonly used;however,each of them has their shortcomings.For example,the absence of different kinds of leakage besides flatus and stool could further lead to underscoring the incontinence severity.Therefore,there has long been a call for a more precise scoring system.The correct identification of the presence and severity of fecal incontinence is paramount for further diagnostic approach and for choosing the appropriate therapy option.This editorial describes fecal incontinence,its effect on quality of life in general and further evaluates the diagnostic approach with a particular focus on symptom scoring systems and their implications for clinical practice.

Estimation of Physiologic Ability and Surgical Stress scoring system for predicting complications following abdominal surgery: A metaanalysis spanning 2004 to 2022

BACKGROUND Postoperative complications remain a paramount concern for surgeons and healthcare practitioners.AIM To present a comprehensive analysis of the Estimation of Physiologic Ability and Surgical Stress(E-PASS)scoring system’s efficacy in predicting postoperative complications following abdominal surgery.METHODS A systematic search of published studies was conducted,yielding 17 studies with pertinent data.Parameters such as preoperative risk score(PRS),surgical stress score(SSS),comprehensive risk score(CRS),postoperative complications,post-operative mortality,and other clinical data were collected for meta-analysis.Forest plots were employed for continuous and binary variables,withχ2 tests assessing heterogeneity(P value).RESULTS Patients experiencing complications after abdominal surgery exhibited significantly higher E-PASS scores compared to those without complications[mean difference and 95%confidence interval(CI)of PRS:0.10(0.05-0.15);SSS:0.04(0.001-0.08);CRS:0.19(0.07-0.31)].Following the exclusion of low-quality studies,results remained valid with no discernible heterogeneity.Subgroup analysis indicated that variations in sample size and age may contribute to hetero-geneity in CRS analysis.Binary variable meta-analysis demonstrated a correlation between high CRS and increased postoperative complication rates[odds ratio(OR)(95%CI):3.01(1.83-4.95)],with a significant association observed between high CRS and postoperative mortality[OR(95%CI):15.49(3.75-64.01)].CONCLUSION In summary,postoperative complications in abdominal surgery,as assessed by the E-PASS scoring system,are consistently linked to elevated PRS,SSS,and CRS scores.High CRS scores emerge as risk factors for heightened morbidity and mortality.This study establishes the accuracy of the E-PASS scoring system in predicting postoperative morbidity and mortality in abdominal surgery,underscoring its potential for widespread adoption in effective risk assessment.

Comparison of Block Design Nonparametric Subset Selection Rules Based on Alternative Scoring Rules

This article compares the size of selected subsets using nonparametric subset selection rules with two different scoring rules for the observations. The scoring rules are based on the expected values of order statistics of the uniform distribution (yielding rank values) and of the normal distribution (yielding normal score values). The comparison is made using state motor vehicle traffic fatality rates, published in a 2016 article, with fifty-one states (including DC as a state) and over a nineteen-year period (1994 through 2012). The earlier study considered four block design selection rules—two for choosing a subset to contain the “best” population (i.e., state with lowest mean fatality rate) and two for the “worst” population (i.e., highest mean rate) with a probability of correct selection chosen to be 0.90. Two selection rules based on normal scores resulted in selected subset sizes substantially smaller than corresponding rules based on ranks (7 vs. 16 and 3 vs. 12). For two other selection rules, the subsets chosen were very close in size (within one). A comparison is also made using state homicide rates, published in a 2022 article, with fifty states and covering eight years. The results are qualitatively the same as those obtained with the motor vehicle traffic fatality rates.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

New objective scoring system to clinically assess fecal incontinence

BACKGROUND Several scoring systems are used to assess fecal incontinence(FI),among which,the most commonly used are Wexner and Vaizey’s scoring systems.However,there are significant lacunae in these scoring systems,due to which they are neither accurate nor comprehensive.AIM To develop a new scoring system for FI that is accurate,comprehensive,and easy to use.METHODS A pro forma was made in which six types of FI were included:solid,liquid,flatus,mucous,stress,and urge.The weight for each FI was determined by asking a group of patients and laypersons to give a disability score to each type of FI from 0 to 100(0-least,100-maximum disability).The disability was assessed on a modified EQ-5D+(EuroQol)description system,4D3L(4 dimensions and 3 levels)for each FI.The average score of each FI was calculated,divided by 10,and rounded off to determine the weight of each FI type.The scores for the three levels of frequency of each FI were assigned as never=0(No episode of FI ever),occasional=1(≤1 episode of FI/wk),and common=2(>1 episode of FI/wk),and was termed as frequency score.The score for each FI would be derived by multiplying the frequency score and the weight for that FI type.In the second phase of the study,a group of colorectal surgeons was asked to rank the six FI types in order of severity,and their ranking was compared with the patient and laypersons’rankings.RESULTS Fifty patients and 50 laypersons participated in the study.The weight was assigned to each FI(solid-8,liquid-8,urge-7,flatus-6,mucus-6,and stress-5),and an new scoring system was formulated.The maximum possible score was 80(total incontinence),and the least 0(no incontinence).The surgeons’ranking of FI severity did not correlate well with patients’and laypersons’rankings of FI,highlighting that surgeons and patients may perceive the severity of FI differently.CONCLUSION A new scoring system for FI was formulated,which was simple,logical,comprehensive,and easy to use,and eliminated previous shortcomings.Patients’and surgeons’perceptions of FI severity of FI did not correlate well.

Acute pancreatitis:A review of diagnosis,severity prediction and prognosis assessment from imaging technology,scoring system and artificial intelligence

Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of AP typically involve the use of imaging technologies,such as computed tomography,magnetic resonance imaging,and ultrasound,and scoring systems,including Ranson,Acute Physiology and Chronic Health Evaluation II,and Bedside Index for Severity in AP scores.Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity,while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications.Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild,moderate,or severe categories,guiding treatment decisions,such as intensive care unit admission,early enteral feeding,and antibiotic use.Despite the central role of imaging technologies and scoring systems in AP management,these methods have limitations in terms of accuracy,reproducibility,practicality and economics.Recent advancements of artificial intelligence(AI)provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data.AI algorithms can analyze large amounts of clinical and imaging data,identify scoring system patterns,and predict the clinical course of disease.AI-based models have shown promising results in predicting the severity and mortality of AP,but further validation and standardization are required before widespread clinical application.In addition,understanding the correlation between these three technologies will aid in developing new methods that can accurately,sensitively,and specifically be used in the diagnosis,severity prediction,and prognosis assessment of AP through complementary advantages.

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud center.This provides a credible data source for enterprise credit scoring,which is one of the most vital elements during the financial decision-making process.Accordingly,this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data.Instead of predicting the credit rating,our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net(rankXGB).To boost the performance,the rankXGB model combines several weak ranking models into a strong model.Due to the high computational cost and the vast amounts of data,we design an edge computing framework to reduce the latency of enterprise credit evaluation.Specially,we design a two-stage deep learning task architecture,including a cloud-based weak credit ranking and an edge-based credit score calculation.In the first stage,we send the electricity consumption data of the evaluated enterprise to the computing cloud server,where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results.In the second stage,the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result,which is used to calculate an absolute credit score by score normalization.The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

Leveraging Geospatial Technology for Smallholder Farmer Credit Scoring

According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.

The Latest Research Progress in the Application of MEWS Scoring System in Clinical Nursing

This paper summarizes the background of the formation of the Modified Early Warning System(MEWS)evaluation system,its current status of clinical teaching applications in different fields of hospitals,and its significance on the medical and nursing career,aiming to provide specific theoretical basis for medical staff and lay a foundation for continuing to carry out related work on MEWS.

New scoring system for acute chest pain risk stratification: Is it worth SVEAT-ing it?

The emergency room is a very potent environment in the hospital.With the growing demands of the population,improved accessibility to health resources,and the onslaught of the triple pandemic,it is extremely crucial to triage patients at presentation.In the spectrum of complaints,chest pain is the commonest.Despite it being a daily ailment,chest pain brings concern to every physician at first.Chest pain could span from acute coronary syndrome,pulmonary embolism,and aortic dissection(all potentially fatal)to reflux,zoster,or musculoskeletal causes that do not need rapid interventions.We often employ scoring systems such as GRACE/PURSUIT/TIMI to assist in clinical decision-making.Over the years,the HEART score became a popular and effective tool for predicting the risk of 30-d major adverse cardiovascular events.Recently,a new scoring system called SVEAT was developed and compared to the HEART score.We have attempted to summarize how these scoring systems differ and their generalizability.With an increasing number of scoring systems being introduced,one must also prevent anchorage bias;i.e.,tools such as these are only diagnosis-specific and not organ-specific,and other emergent differential diagnoses must also be kept in mind before discharging the patient home without additional workup.

Prognostic value of preoperative immune-nutritional scoring systems in remnant gastric cancer patients undergoing surgery

BACKGROUND Remnant gastric cancer(GC)is defined as GC that occurs five years or more after gastrectomy.Systematically evaluating the preoperative immune and nutritional status of patients and analyzing its prognostic impact on postoperative remnant gastric cancer(RGC)patients are crucial.A simple scoring system that combines multiple immune or nutritional indicators to identify nutritional or immune status before surgery is necessary.AIM To evaluate the value of preoperative immune-nutritional scoring systems in predicting the prognosis of patients with RGC.METHODS The clinical data of 54 patients with RGC were collected and analyzed retrospectively.Prognostic nutritional index(PNI),controlled nutritional status(CONUT),and Naples prognostic score(NPS)were calculated by preoperative blood indicators,including absolute lymphocyte count,lymphocyte to monocyte ratio,neutrophil to lymphocyte ratio,serum albumin,and serum total cholesterol.Patients with RGC were divided into groups according to the immune-nutritional risk.The relationship between the three preoperative immune-nutritional scores and clinical characteristics was analyzed.Cox regression and Kaplan–Meier analysis was performed to analyze the difference in overall survival(OS)rate between various immune-nutritional score groups.RESULTS The median age of this cohort was 70.5 years(ranging from 39 to 87 years).No significant correlation was found between most pathological features and immune-nutritional status(P>0.05).Patients with a PNI score<45,CONUT score or NPS score≥3 were considered to be at high immune-nutritional risk.The areas under the receiver operating characteristic curves of PNI,CONUT,and NPS systems for predicting postoperative survival were 0.611[95%confidence interval(CI):0.460–0.763;P=0.161],0.635(95%CI:0.485–0.784;P=0.090),and 0.707(95%CI:0.566–0.848;P=0.009),respectively.Cox regression analysis showed that the three immunenutritional scoring systems were significantly correlated with OS(PNI:P=0.002;CONUT:P=0.039;NPS:P<0.001).Survival analysis revealed a significant difference in OS between different immune-nutritional groups(PNI:75 mo vs 42 mo,P=0.001;CONUT:69 mo vs 48 mo,P=0.033;NPS:77 mo vs 40 mo,P<0.001).CONCLUSION These preoperative immune-nutritional scores are reliable multidimensional prognostic scoring systems for predicting the prognosis of patients with RGC,in which the NPS system has relatively effective predictive performance.

Intelligent Vehicular Communication Using Vulnerability Scoring Based Routing Protocol

Internet of Vehicles(IoV)is an intelligent vehicular technology that allows vehicles to communicate with each other via internet.Communications and the Internet of Things(IoT)enable cutting-edge technologies including such self-driving cars.In the existing systems,there is a maximum communication delay while transmitting the messages.The proposed system uses hybrid Cooperative,Vehicular Communication Management Framework called CAMINO(CA).Further it uses,energy efficient fast message routing protocol with Common Vulnerability Scoring System(CVSS)methodology for improving the communication delay,throughput.It improves security while transmitting the messages through networks.In this research,we present a unique intelligent vehicular infrastructure communication management framework.This framework includes additional stability for both short and long-range mobile communications.It also includes built-in cooperative intelligent transport system(C-ITS)capabilities for experimental verification in real-world contexts.In addition,an energy efficient-fast message distribution routing protocol(EE-FMDRP)has been presented.This combines the benefits between both temporal and direction oriented routing methods.This has been suggested for distributing information from the origin ends to the predetermined objective in a quick,accurate,and effective manner in the event of an emergency.The critical value scale score(CVSS)employ ratings to measure the assault probability in Markov chains.Probabilities of chained transitions allow us to statistically evaluate the integrity of a group of IoVassets.Thus the proposed method helps to enhance the vehicular systems.The CAMINO with energy efficient fast protocol using CVSS(CA-EEFP-CVSS)method outperforms in terms of shortest transmission latency achieves 2.6 sec,highest throughput 11.6%,and lowest energy usage 17%and PDR 95.78%.

Prognostic and diagnostic scoring models in acute alcoholassociated hepatitis:A review comparing the performance of different scoring systems

Alcohol-associated hepatitis(AAH)is a severe form of liver disease caused by alcohol consumption.In the absence of confounding factors,clinical features and laboratory markers are sufficient to diagnose AAH,rule out alternative causes of liver injury and assess disease severity.Due to the elevated mortality of AAH,assessing the prognosis is a radical step in management.The Maddrey discriminant function(MDF)is the first established clinical prognostic score for AAH and was commonly used in the earliest AAH clinical trials.A MDF>32 indicates a poor prognosis and a potential benefit of initiating corticosteroids.The model for end stage liver disease(MELD)score has been studied for AAH prognostication and new evidence suggests MELD may predict mortality more accurately than MDF.The Lille score is usually combined to MDF or MELD score after corticosteroid initiation and offers the advantage of assessing response to treatment a 4-7 d into the course.Other commonly used scores include the Glasgow Alcoholic Hepatitis Score and the Age Bilirubin international normalized ratio Creatinine model.Clinical AAH correlate adequately with histologic severity scores and leave little indication for liver biopsy in assessing AAH prognosis.AAH presenting as acute on chronic liver failure(ACLF)is so far prognosticated with ACLF-specific scoring systems.New artificial intelligence-generated prognostic models have emerged and are being studied for use in AAH.Acute kidney injury(AKI)is one possible complication of AAH and is significantly associated with increased AAH mortality.Predicting AKI and alcohol relapse are important steps in the management of AAH.The aim of this review is to discuss the performance and limitations of different scoring models for AAH mortality,emphasize the most useful tools in prognostication and review predictors of recurrence.

Selection of Surgical Approach and Clinical Significance of Lower Cervical Spine Injuries Guided by SLIC Scoring System

Objective: To explore the feasibility and clinical significance of surgical approach selection for cervical spine injury guided by SLIC scoring system. Methods: The clinical data of 75 patients with lower cervical injury surgery from January 2020 to November 2022 were retrospectively analyzed, including 48 males and 27 females. Age: 28 - 65 years old. Causes of injury: 39 cases of traffic accidents, 15 cases of ice and snow sports, 12 cases of falling from high places, 9 cases of heavy objects. There were 12 cases of C3-4, 33 cases of C4-5, 21 cases of C5-6, and 9 cases of C6-7. Time from injury to medical treatment: 4 h - 2 d. Cervical spine X-ray, MRI, MDCT examination and preoperative SLIC score were performed on admission. Anterior approach was performed by subtotal cervical vertebrae resection or discectomy, titanium Cage or cage supported bone grafting and anterior titanium plate fixation. Posterior approach was performed with cervical laminoplasty, lateral mass or pedicle screw fixation and fusion. The combined anterior-posterior operation was performed by the anterior methods+ posterior methods. The time from injury to surgery is 12 h to 3 d. The function before and after operation was evaluated by JOA efficacy evaluation criteria. The correlation between the three surgical approaches and postoperative efficacy and SLIC score was compared. SPSS 22.0 software was used for statistical analysis of the data. Results: In this group of 75 patients, 32 cases of anterior operation, 22 cases of posterior operation and 21 cases of combined operation were followed up for no less than 12 months. There was no significant difference in age, gender, injury cause, injury segment, time from injury to treatment, and time from injury to operation among the three surgical approaches, which were comparable. The SLIC scores of mild, moderate and severe injuries of anterior surgery, posterior surgery and combined anterior and posterior surgery, They were (5.26 ± 1.24, 5.86 ± 1.67, 8.25 ± 0.21), (5.57 ± 1.43, 5.99 ± 1.85, 9.00 ± 0.25), (0, 5.98 ± 0.33, 9.44 ± 0.34), respectively. By comparing the SLIC scores and JOA scores of anterior surgery and posterior surgery, there was no difference in SLIC scores and JOA scores between the two groups for mild and moderate injuries (P > 0.05). However, the JOA scores at 3 months, 6 months and 12 months after surgery were different from those before surgery, and the postoperative efficacy and JOA scores were significantly improved (P & lt;0.05), indicating that the two surgical methods had the same therapeutic effect, that is, anterior or posterior surgery could be used to treat mild or moderate injuries (P > 0.05). There were differences in SLIC scores among the three surgical approaches for severe injury (P 0.05). The postoperative efficacy and JOA score of combined anterior-posterior approach were significantly improved compared with those before operation (P Conclusion: SLIC score not only provides accurate judgment for conservative treatment or surgical treatment of cervical spine injury, but also provides evidence-based medical basis and reference value for the selection of surgical approach and surgical method. According to the SLIC score, the surgical approach is safe and feasible. When the SLIC score is 4 - 7, anterior surgery is selected for type A injury, and posterior surgery is selected for type B injury. When the SLIC score is ≥8, combined anterior-posterior surgery should be selected. It is of great significance for clinical formulation of precision treatment strategy.

Effectiveness of Gardnerella vaginalis culture and Nugent scoring in identifying bacterial vaginosis in pregnant women

Background:Bacterial vaginosis(BV),a lower genital tract syndrome,has been linked to adverse pregnancy outcomes and is regarded as the prevalent type of vaginal infection in females of childbearing age.Objectives:To determine the accuracy of Nugent scoring and Gardnerella vaginalis culture in the diagnosis of bacterial vaginosis(BV)among pregnant women attending antenatal clinic in Nnamdi Azikiwe University Teaching Hospital,Nnewi,Nigeria.Method:This cross-sectional study evaluated biospecimen from 333 pregnant women enrolled through systematic random sampling technique.Biospecimens of vaginal discharge were tested for BV infection using Amsel’s criteria,Nugent’s score and culture of G.Vaginalis.Using Amsel’s criteria as a“gold standard”,the Nugent’s score and culture of G.vaginalis were estimated.Results:Prevalence of 26.12%,25.82% and 28.20% of BV was found using Amsel criteria,Nugent’s method and culture of G.vaginalis,respectively.No statistical relationship exists between socio-demographic characteristics and BV(P>0.05).Sexual exposure,and vaginal hygienic practices influences BV(P<0.05)infection and also with fishy odor during or after sexual intercourse,Gardnerella morphotypes,Bacteroides morphotypes and BV(P<0.05).An inverse relationship existed between lactobacilli morphotypes and BV.The prevalence of HIV was 5.41% and 16 out of 18 had BV diagnosed using Amsel criteria.Nugent method correlated strongly with Amsel criteria(P<0.05).In comparison with Amsel criteria,it had 78.16% sensitivity,92.68% specificity,79.07% positive predictive value,92.31% negative predictive value and 88.89% accuracy rate.This was in contrast distinction to the culture of G.vaginalis,which had 56.32%sensitivity,81.70% specificity,52.13% positive predictive value,84.10% negative predictive value and 75.08% accuracy rate.Conclusion:Nugent method correlated strongly with Amsel criteria(P<0.05)and had 78.16% sensitivity,92.68% specificity,79.07% positive predictive value,92.31% negative predictive value and 88.89% accuracy rate.Contrariwise,the culture of G.vaginalis had 56.32% sensitivity,81.70% specificity,52.13% positive predictive value,84.10% negative predictive value and 75.08% accuracy rate for BV diagnosis.

Relationship between Pronuclear Scoring and Embryo Quality and Implantation Potential in IVF-ET

To assess the relationship between pronuclear scoring and day-3 embryo quality and pregnancy outcome and to determine the Clinical value of pronuclear stage scoring system in human in vitro fertilization-embryo transfer （IVF-ET） program, a pronuclear scoring system was used to score zygotes 16-20 h after insemination during conventional IVF or intracytoplasmic sperm injection （ICSI）. The embryos were classified into groups Z1, Z2, Z3 and Z4, Comparisons were made of the rates of arrested embryos and excellent embryos on day 3. Comparisons of pregnancy outcome were made only in those patients in whom cohorts of similarly Z-scored embryos were transferred, The results showed that there were less arrested embryos and more excellent embryos on day 3 in groups Z1 and Z2 than those in group Z3 and Z4, More embryos arrested and less excellent embryos developed in group Z4 than group Z3. The clinical pregnancy rates resulting from the transfer of single pronuclear score homologous embryo types were similar among groups Z1, Z2 and Z3. Implantation rates of group Z1 were higher （P〈0.05） than that of group Z3, These findings suggests that pronuclear scoring can predict developmental ability on day 3 and implantation potential. A evaluation that combines the Z-score and day 3 embryo morphology is useful in the determination of the most viable embryos and the number of embryos for transfer.

A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Although psychometric features have been considered for alternative credit scoring,they have not yet been applied to peer-to-peer(P2P)lending because such information is not available on platforms.This study proposed an alternative credit scoring model for P2P lending by extracting typical personality types inferred from the borrowers’job category.We projected a virtual space of borrowers by using the affinity matrix based on the Myers–Briggs type indicator(MBTI)that fits each job category.Applying the distance in this space to Lending Club data,we used locally weighted logistic regression to vary the coefficients of the variables,which affect loan repayments,with each MBTI type for predicting the default probability.We found that each MBTI type’s credit scoring model has different significant variables.This study provides insights into breakthroughs in developing alternative credit scoring for P2P lending.

Default or profit scoring credit systems?Evidence from European and US peer-to-peer lending markets

For the emerging peer-to-peer(P2P)lending markets to survive,they need to employ credit-risk management practices such that an investor base is profitable in the long run.Traditionally,credit-risk management relies on credit scoring that predicts loans’probability of default.In this paper,we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans.To validate our profit scoring models with traditional credit scoring models,we use data from a European P2P lending market,Bondora,and also a random sample of loans from the Lending Club P2P lending market.We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following:logistic and linear regression,lasso,ridge,elastic net,random forest,and neural networks.We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans.More specifically,as opposed to credit scoring models,returns across all loans are 24.0%(Bondora)and 15.5%(Lending Club)higher,whereas accuracy is 6.7%(Bondora)and 3.1%(Lending Club)higher for the proposed profit scoring models.Moreover,our results are not driven by manual selection as profit scoring models suggest investing in more loans.Finally,even if we consider data sampling bias,we found that the set of superior models consists almost exclusively of profit scoring models.Thus,our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models.