Screening Process Research Articles

Resume Optimization Model Using Machine Learning Techniques

In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model's decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates' qualifications and job requirements.

A national survey of first line antibiotic use in neonatal units - and the potential scope for iatrogenic sensorineural hearing loss prevention.

National Institute for Clinical Excellence (NICE) guidance for the management of neonatal sepsis recommends a first-line antibiotic regimen containing an aminoglycoside (gentamicin). Aminoglycoside exposure causes sensorineural hearing loss in individuals with a specific mitochondrial genetic variant (m.1555A>G). This variant can be detected promptly (in <30 min) by a point of care test. NICE does allow for variation in antibiotic regimes depending on local microbiology guidance. As practices can vary, this survey aimed to determine the current use of first-line antibiotic agents within neonatal units and postnatal wards across the UK. A telephone survey was conducted across all neonatal units in the United Kingdom. Responses were requested from a member of the neonatal team experienced in neonatal septic screening processes. One response was recorded per unit. Of the 187 neonatal units, 186 (99%) responded to the survey. One unit declined to participate. The survey results show most neonatal units (93%) and postnatal wards (74%) across the United Kingdom use aminoglycosides as first-line antibiotic agents. Antibiotic regimes varied between different units and between different locations within the same hospital (NICU vs. postnatal wards). In cases where there was a contraindication to aminoglycosides, the most common alternative antibiotic was cefotaxime. Most neonatal units in the UK use an aminoglycoside antibiotic as first-line agent for suspected sepsis. This places infants with the m.1555A>G genetic variant at risk of iatrogenic hearing loss. There needs to be integration of point-of-care genetic testing within the neonatal septic screening pathway.

The association between cancer literacy and cancer screening behaviours: a systematic review

Abstract Background This study aimed at investigating the association between cancer literacy (CL) and adherence to cancer screening programs. Methods PubMed, Scopus and Web of Science were searched. Any study, published until March 2024, that investigated the associations between CL and cancer screening intention or uptake in any target population were eligible. For CL measurement, both validated and ad hoc tools were considered. For each outcome, articles were grouped according to the type of cancer investigated and results were narratively synthesized. Results A total of 3426 records were retrieved. After deduplication and screening process, six studies were included. Population enrolled was heterogeneous as well as the tool used to measure CL. Four studies explored the relationship between CL and screening uptake, one focused solely on screening intention and another investigated both intention and uptake. Significant association was found between CL and screening intention in the two studies included, with higher literacy levels correlating to increased intent for screening. A significant association between higher CL and skin cancer screening uptake was reported, while the uptake of prostate cancer screening was not significantly associated with CL levels. By contrast, mixed results were obtained investigating breast, cervix and colorectal screening uptake. Conclusions Higher levels of cancer literacy appeared to be associated with screening intention, while its association with screening uptake was found barely significant for any screening type, suggesting that other factors influence the actual screening uptake. However, the scarce number of studies, along with the absence of validated tools and the heterogeneity of target populations, limits the generalizability of the results. Further research, differentiating screening types and timelines and using multivariate models, is needed to clarify the role of CL in screening behaviours. Key messages • Higher levels of cancer literacy appear to be associated with screening intention, suggesting its importance in promoting motivation for early cancer detection. • Association between cancer literacy and screening uptake was found barely significant for any screening type, suggesting that other factors influence actual screening uptake, urging further research.

Efficacy of digital interventions for smoking prevention among children: a systematic review

Abstract Background Smoking remains a leading preventable cause of morbidity and mortality globally, with initiation often occurring in childhood. Early smoking onset is linked to increased difficulty in quitting and higher risk of long-term health complications. Traditional prevention strategies have not fully succeeded in curbing youth smoking rates, highlighting the need for innovative approaches. Digital interventions (DI), such as gamified education, social media campaigns, and virtual reality, offer a novel avenue for engagement and education. This systematic review aims to assess the efficacy of DI in preventing smoking, and improving knowledge and awareness about smoking damages, among children. This review is part of the PRIN project, whose goal is to evaluate the effectiveness of a DI aimed at school-aged children, intended to increase knowledge about the negative effects of smoking and prevent its use. Methods On the 18th of March 2024, PubMed, Scopus, and PsycINFO were surfed for trials and observational studies evaluating DI for smoking prevention among children aged 6-12 years. Study quality will be evaluated using the Cochrane Risk of Bias Tool and New Castle-Ottawa scale. The protocol has been registered in PROSPERO. Results From 3,081 initially identified articles, 328 papers were removed because duplicate. The screening process is ongoing. Conclusions DI are a promising tool for engaging young populations through platforms they are familiar with and receptive to. By leveraging the high penetration of digital technology in the daily lives of children, these interventions have the potential to deliver smoking prevention messages in a compelling and interactive manner. Understanding the effectiveness of DI in smoking prevention can significantly contribute to public health by providing viable alternatives to traditional methods and potentially reducing future smoking-related health burdens. Key messages • Evaluating digital strategies for smoking prevention among children could offer vital alternatives to traditional methods, potentially reducing long-term health risks. • Digital interventions (DI) offer promising new ways to engage young audiences on smoking prevention using familiar platforms, potentially reshaping public health approaches.

Unveiling the clinical profiles of heart failure across diverse stages and phenotypes: insights from the national echocardiographic society registry

Abstract Background The modern comprehension of heart failure (HF), based on a staging paradigm, demands systematic screening to promptly identify individuals at risk, those in the pre-HF stage, and symptomatic HF patients across various phenotypes (HFpEF, HFmrEF, and HFrEF). The discernment of clinical profiles within these categories holds significant potential for improving screening and ensuring timely therapeutic interventions. Material and Methods Our national echocardiographic society executed a multicenter HF screening program. General practitioners in 13 primary care centers utilized the original mobile app to determine referrals for transthoracic echocardiography and N-terminal pro-B-type natriuretic peptide (NT-proBNP) testing in patients without a prior HF diagnosis. The 2021 ESC guidelines' algorithms were employed for HF diagnosis (HFpEF, HFmrEF, HFrEF). This study identifies two pre-heart failure (pre-HF) stages: "heart stress" (elevated NT-proBNP in asymptomatic individuals with risk factors, regardless of structural heart disease or cardiac dysfunction) and individuals "at risk for HF" (normal NTproBNP and at least one HF risk factor). Ultimately, all patients were categorized into six groups: (I) no HF, no risk; (II) at risk for HF; (III) heart stress; (IV) HFpEF; (V) HFmrEF; and (VI) HFrEF. Subsequently, the eleven clinical variables were compared among groups (II) to (VI) using chi-square test. Results Among 930 screened outpatients (mean age 66±11 years, 61% female), diagnosis of HF was established in 34.2% (Figure, pie). Heart stress was found in 22.3% patients, and 38.1% were at risk for HF, while 5.5 % had neither HF nor risk factors. Group comparison revealed that older individuals were more frequent in the HFpEF group, females predominated in the heart-stress and HFpEF groups, and males in the HFrEF group (Figure, table). Obesity was the most frequent among those at risk and HFmrEF. Atrial fibrillation and pathological ECG increased gradually from those at risk to HFrEF. Previous MI was the most common in heart stress and HFmrEF groups, while arterial hypertension &amp;gt;10 years prevailed in HFpEF and HFmrEF patients. Diabetes mellitus &amp;gt; 5 years was present in 24-28% across groups. CKD or eGFR≤60 ml/min/1.73m² increased gradually towards HFmrEF. The family history of CMP or SCD was positive in 17-24%, with no significant differences observed across groups. Additionally, a small number of patients had a history of previous cardiotoxic cancer treatment, and no significant differences were found between groups. Conclusions The mobile phone application used in primary care successfully identified a significant number of pre-heart failure patients requiring further evaluation. Different HF stages and phenotypes exhibit distinct clinical profiles, aiding in the screening process and contributing to a deeper understanding of the heart's pathophysiological adaptation to diverse risk factors during HF development.

Capability of ChatGPT to support the screening process of scoping reviews: A feasibility study

Abstract Background The time-consuming screening phase in health-related evidence syntheses is increasingly supported by artificial intelligence (AI). However, scoping reviews have not benefited as much as systematic reviews from such AI tools as they utilize conceptual rather than keyword-specific search strategies to address broad research questions. Context-understanding chatbots based on large language models could potentially enhance the efficiency of scoping review screenings. This study evaluates the performance of ChatGPT against an open-access AI tool used for abstract screening in systematic reviews and the costs involved. Methods Leveraging data from a prior scoping review, we compared the performance of ChatGPT 4.0 and 3.5 against Rayyan, using human researchers’ decisions as a benchmark. A random set of 50 included and 50 excluded abstracts was used to train Rayyan’s algorithm and to develop ChatGPT’s prompt. ChatGPT 4.0’s evaluation was repeated after 5-7 days to assess response consistency. We computed performance metrics including sensitivity, specificity, and accuracy. Results ChatGPT 4.0 and 3.5 achieved 68% accuracy, 11% precision, 99% negative predictive value, and 67% specificity. Sensitivity was high at 88-89% for ChatGPT 4.0 and 84% for ChatGPT 3.5. ChatGPT 4.0 showed a substantial interrater reliability between the two evaluations and moderate reliability compared to ChatGPT 3.5. The cost of deployment varied, with Rayyan being free, ChatGPT 3.5 costing $9.06 and ChatGPT 4.0 $505.72. Conclusions Given the exponential increase in publications, effective mechanisms to support the screening phase of scoping reviews are needed. Our feasibility study using ChatGPT to decide on abstracts’ inclusion or exclusion achieved good performance metrics. Given further positive evaluation, such chatbots might be incorporated in the standard screening process, possibly replacing a second screener, saving time and costs, and accelerating evidence synthesis. Key messages • ChatGPT performs well in supporting screening for scoping reviews, outperforming a traditional AI tool at reasonable costs. • Employing chatbots like ChatGPT could potentially cut costs and time in scoping reviews.

Using artificial intelligence to support and streamline rapid systematic evidence reviews

Abstract Issue The Rapid Evidence Service, initiated by the National Collaborating Centre for Methods and Tools (NCCMT) during COVID-19, supports public health decision making by conducting rapid reviews on priority topics. Description of issue Integral to rapid reviews is an expedited timeline but the quantity of available literature for most public health review questions takes significant time to screen manually. NCCMT integrated 4 artificial intelligence (AI) features into the screening process. DAISY Rank applies predictions learned from manual screening patterns to re-order remaining studies, with most relevant appearing first. AI Screening automatically screens remaining studies based on prediction scores. Check for Screening Errors and Re-Rank Report use previous screening patterns to identify studies that were potentially falsely excluded and predict the total number of included studies, respectively. These features were tested by comparing results provided by AI with those produced manually for select test sets. Results NCCMT used AI to support and expedite screening, assess screening progress, and/or minimize risk of inappropriately excluding studies for 35 rapid reviews on 20 topics. Using DAISY Rank enabled one screener to review over 4000 references in 9 hours, compared to a different review, where the same amount of screening took 28 hours without DAISY Rank. AI Screening correctly excluded up to 80% of irrelevant search results across reviews. Check for Screening Errors identified 37 potential includes manually excluded in one review; these were reviewed and 3 were included. Re-Rank Report allowed NCCMT to re-allocate staff to subsequent steps in the review process when most included studies were identified. Lessons Integrating AI features into screening led to less time required, better anticipated timelines, more accurate staff allocation and reduced errors. More rigorous study of AI best practices is needed to continue to improve rapid review method efficiencies. Key messages • Rapid reviews can be an important source of evidence for decision makers if they can be completed quickly but maintain rigor and accuracy. • AI holds promise as a way to improve screening efficiency.

How characteristics of place affect multimorbidity: a systematic scoping review

Abstract Background Multimorbidity has become a global public health challenge. Increasing work has identified different individual- and household-level determinants of multimorbidity; however, our understanding of how characteristics of place affect multimorbidity remains limited. This scoping review aims to identify the place-based risk factors of multimorbidity and to synthesis evidence (longitudinal in particular) on how place-based risk factors affect multimorbidity. Methods Following PRISMA-ScR guidelines, a predefined search strategy (https://osf.io/bf84m) was run across 7 major databases in 2023, with a double screening and data extraction process. English-language peer-reviewed studies exploring the relationship between place-based characteristics (from neighbourhood to region) and multimorbidity among the general population aged over 18 years old were included from 2010 to 2023. Results Out of 7761 records, 129 underwent full-text review, resulting in 76 included studies (15 longitudinal). We identified 12 types of place-based risk factors of multimorbidity, with area-level deprivation/SES, pollution, and urban/rurality most frequently examined. Fairly consistent findings suggested that people in more deprived, polluted, or urbanised areas were more likely to be multimorbid, which may further vary by multimorbidity definitions/measurements. Other place-based risk factors, such as social cohesion and greenspace availability, were linked to different single health conditions, but their associations with multimorbidity were underexplored. Finally, we proposed a potential theoretical framework that links place and multimorbidity through multiple pathways based on the synthesised evidence. Conclusions There remains only a partial understanding of how ‘place’ affects multimorbidity. Future studies should employ more precise measures for both place-based risk factors and multimorbidity and use longitudinal designs and analytical approaches for more causal insights. Key messages • This is the first scoping review that proposed a theoretical framework that demonstrates the links between place and multimorbidity. • This study will greatly benefit local policymaking and public health intervention initiation.

The influence of remnant cholesterol on ischemic stroke and its subtypes

Abstract Background Established studies have firmly identified elevated low-density lipoprotein (LDL) as a key risk factor for stroke. Nevertheless, an emerging body of evidence suggests that residual stroke risk endures even with rigorous LDL control. Recent investigations propose a potential pivotal role of remnant cholesterol (RC) in this lingering risk. However, the causative relationship between RC and ischemic stroke, encompassing its subtypes, remains elusive. In this study, we employed a two-sample Mendelian Randomization (MR) analysis to delve into the causal association between RC and ischemic stroke, aiming to shed light on its various subtypes. Methods We sourced single nucleotide polymorphism (SNP) data associated with stroke and its subtypes from the MEGASTROKE consortium's comprehensive genome-wide association study (GWAS). SNPs relevant to RC were acquired from the MRC Integrative Epidemiology Unit (IEU) Open GWAS database. Our investigation involved the evaluation of the correlation between RC and ischemic stroke, encompassing 40,585 patients and 406,111 controls. Subtypes considered included large artery atherosclerosis (n=4,373), small artery occlusion (n=5,386), and cardioembolic stroke (n=7,193). A meticulous screening process was implemented to select instrumental variables (IVs) for RC, followed by five Mendelian Randomization (MR) analyses utilizing inverse-variance-weighted (IVW), weighted median, MR-Egger regression, simple mode, and weighted mode methods. Rigorous sensitivity analyses, along with assessments for heterogeneity and pleiotropy, were conducted to ensure the robustness and credibility of our study results. Results Our investigation unveiled a causal relationship between RC and large artery atherosclerosis-type ischemic stroke (IVW method, OR=1.267, 95% CI: 1.017-1.578, P=0.035). This association was further substantiated through rigorous sensitivity analyses, assessments for heterogeneity, and pleiotropy tests. In contrast, no evidence supported a causal link between RC and overall ischemic stroke (IVW method, OR=1.072, 95% CI: 0.986-1.164, P=0.102), small artery occlusion-type stroke (IVW method, OR=1.127, 95% CI: 0.939-1.353, P=0.198), and cardioembolic stroke (IVW method, OR=1.083, 95% CI: 0.931-1.259, P=0.301). Conclusions Our findings robustly establish a causal connection between elevated levels of RC and the occurrence of large artery strokes. This highlights the potential efficacy of reducing RC as a preventive strategy against large artery atherosclerosis. Nevertheless, it is crucial to note that such mitigation efforts may not necessarily result in a decreased risk associated with small artery occlusion and cardioembolic strokes.

Depressiveness in German higher education students – a systematic review and meta-analysis

Abstract Introduction Evidence shows that German higher education students are vulnerable to depressive symptoms. However, there is no recent review compiling these findings. Thus, our study aims at describing the extent to which higher education students in Germany are affected by depressive symptoms. Methods We searched three databases for suitable publications using three core concepts (‘depressive symptoms’, ‘students’, and ‘Germany’). The inclusion criteria were: (a) the population studied consisted of students from higher education institutions, (b) data was assessed at higher education institutions in Germany, (c) information on depressive symptoms were pro-vided as outcome of instruments assessing depressive symptoms, (d) articles were published in peer-reviewed journals, in (e) English or German and (f) between 2002 and 2023. Random effects models were used to calculate both general and subgroup-specific pooled prevalence rates. Subgroups were formed based on the included literature (e.g. gender, before or during the COVID-19 pandemic, study phase and programme). Results The literature search resulted in 992 articles. After the removal of duplicates and the two-stage screening process, prevalence data was extracted from 60 articles. The overall pooled prevalence rate was 21.1% (95% CI: 17.6% - 24.6%). Medical students (13.2%; 95% CI: 9.9% - 16.4%) and first-year students (11.0%; 95% CI: 7.7% - 14.3%) were less likely to be affected than students in general. Women (29.0%; 95% CI: 21.4% - 36.5%) were more prone to depressive symptoms than men (23.1%; 95% CI: 16.5% - 29.6%). In samples collected during the COVID-19 pandemic, the pooled prevalence rates were higher than in pre-pandemic times (30.6%; 95% CI: 22.1% - 39.1% vs. 18.0%; 95% CI: 14.7% - 21.2%). Conclusions This analysis emphasizes the pressing need to address the mental health of higher education students both at the policy level and within universities. Key messages • German higher education students show alarming levels of depressive symptoms, demanding immediate action. • During the pandemic mental health was even poorer than before, highlighting the need for tailored interventions and support in challenging times.

Bridging the gap: mapping co-creation methods for public health impact and innovation

Abstract Background Co-creation offers a promising approach to public health innovation, particularly for tackling complex issues. However, the lack of a standardized methodology limits its reliability and reproducibility, especially across diverse contexts. This study explores co-creation methods used by researchers and practitioners, aiming to shed light on their implications for public health. Understanding this methodological landscape can enhance the effective implementation of co-creation processes to address complex challenges. Methods Utilizing the Systematic Methods Overview approach, we examined co-creation methods. Data were gathered from the Health CASCADE Co-Creation Database and grey literature using an artificial intelligence-assisted recursive search strategy and a two-step screening process. We conducted textual, comparative, and bibliometric analyses to evaluate methodological underpinnings and relationships between methods. Results Analysis of 2,627 academic articles and grey literature sources identified over 900 co-creation methods. While surveys, focus groups, and group discussions were prevalent, participatory methods were underutilized. Notably, 91.3% of methods co-occurred, with a predominant combination of qualitative approaches. Furthermore, a significant discrepancy between research and practice was evident, with only 10.2% convergence. Conclusions This study revealed a critical gap between co-creation in research and practice, emphasizing the need to bridge this methodological divide for advancing co-creation as a reliable approach in public health. The over-reliance on qualitative methods raises concerns about tokenistic engagement and undermines meaningful public participation in co-creation. Addressing this trend is vital to preserve the essence of true co-creation and maximize its potential for addressing complex public health challenges. Key messages • Mapping the diverse methods of co-creation in public health reveals both richness and disparities in approach, from surveys to participatory methods. • Addressing the discrepancy between co-creation in research and practice is vital for establishing a reliable and effective approach to tackling public health challenges.

Gastric Cancer associated with the use of Omeprazole in Adults

Introduction: proton pump inhibitors constitute a group of drugs used to suppress stomach acid secretion and whose consumption has intensified worldwide due to their consideration as the most effective gastric protectors, omeprazole being one of the most widely used. Objectives: to analyze the existence of gastric cancer risk due to long-term use of omeprazole in adults.Methods: a search for information was carried out in PubMed and Google Scholar. Search formulas were used by combining the terms: “Omeprazole”, “Cancer Gastric”, “Omeprazole and Adverse effects”, “Omeprazole and Helicobacter Pylori”. From the total number of articles identified, after the screening process, 8 articles were chosen for the present study.Results: Prolonged treatment with omeprazole can induce histological changes in the gastric mucosa, including hyperplasia, increased gastrin-producing cells, atrophic gastritis and increased colonization of Helicobacter pylori. A rebound acid hypersecretion has been observed after discontinuation of treatment, persisting for at least two months. Endocrine hyperplasia may result from suppression of acid secretion, causing hypergastrinemia and increasing the risk of malignant lesions.Conclusions: information has been compiled on the effects of prolonged use of omeprazole and its relationship with gastric carcinoma. This use is becoming more frequent, which underlines the need to investigate its long-term effects, since it may alter gastric histoarchitecture and predispose to cancer

Lumpy Skin Disease: A review of epidemiological study and preventive measures

Background: Lumpy skin disease (LSD) is a highly infectious and economically important transboundary disease that is rapidly spreading to the globe. The disease causes high morbidity and a low mortality rate of infection. The animals show acute or chronic illnesses depending on the immune responses of the hosts. The economic burden of LSD manifested the poor-quality hides, a drop in milk and meat production, abortion, and death. Methods: This systematic literature review was accomplished according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature on lumpy skin disease has been explored over the last two decades and searched keywords on online databases such as Google Scholar, PubMed, and Scopus. Both automated and manual searching tools were used to screen the articles. The literature published other than English was rejected during the screening process. Conference papers were excluded during the screening. Results: LSD is transmitted by blood-sucking arthropods and is most prevalent in summer and rainy seasons. Exotic-bred cattle and calves are highly susceptible. Although the disease has low mortality, the high mortality of the disease prevails in endemic regions in complicated cases. Calves from unvaccinated cows should be vaccinated at any age, on the other hand, the calves from vaccinated cows should be immunized at 3 months of age. Animals should be vaccinated before the risk period. Strict biosecurity, quarantine, and immunoprophylaxis can reduce the prevalence of the disease. Conclusions: Lumpy skin disease (LSD) is an acute infectious and contagious disease affecting cattle and water buffaloes. The disease causes serious economic loss due to decreased production, skin problems, and mortality in complicated cases. Further details on genetic characterization, transmission dynamics, and host-pathogen interaction should be performed to prevent the prevalence of the disease in emerging or re-emerging countries.

Accuracy enhancement of metabolic index-based blood glucose estimation with a screening process for low-quality data.

Many researchers have proposed various non-invasive glucose monitoring (NIGM) approaches using wearable or portable devices. However, due to the limited capacity of detectors for such compact devices and the movement of the body during measurement, the precision of the acquired data frequently diminishes, which can cause problems during actual use in daily life. In addition, intensive smoothing is often used in post-processing to mitigate the effects of erroneous values. However, this requires a considerable amount of data and results in a delay in the response to the actual blood glucose level (BGL). Instead of just applying data smoothing in the post-process of the data acquisition, we propose an active low-quality data screening method in the pre-process. In the proposal phase of the screening process, we employ an analytical approach to examine and formulate factors that might affect the BGL estimation accuracy. A signal quality index inspired by the standard deviation concept is introduced to detect visually apparent noise on signals. Furthermore, the total estimation error in the metabolic index (MI) is calculated based on potential perturbations defined by the signal-to-noise ratio (SNR) and the uncertainty due to discrete sampling. Thereafter, the acquired data were screened by these quality indices. By applying the proposed data screening process to the data obtained from a commercially available smartwatch device in the pre-process, the estimation accuracy of the MI-based BGL was improved significantly. Adopting the proposed screen process improves BGL estimation accuracy in the smartwatch-based prototype. Applying the proposed screen process will facilitate the integration of wearable and continuous BGL monitoring into size- and SNR-limited devices such as smartwatches and smart rings.

Assessing the effectiveness of academic coaching interventions for student success in higher education: A systematic review

ABSTRACT Every student has the potential to change the world by making a significant contribution to society. Experiences in higher education significantly shape the development of this potential, for better or worse, leading to interventions to support student success. Among such interventions, academic coaching has been widely used; however, how do we know that it is effective? In this systematic literature review, we describe the characteristics of effective academic coaching interventions, including the format of coaching (individual or group coaching), coaching mode (in-person or distance via technology), coaching topics, coach background, duration, frequency. Focusing on peer-reviewed English journals between 2010 and May 2023, the study found 643 articles, 25 of which met the inclusion criteria following rigorous screening and consensus-reaching processes. Based on the findings, effective coaching interventions span at least 12 hours over 4 weeks, are preferably in-person, and are best facilitated by professional coaches.

Music Therapy for Pain and Anxiety Reduction in Pediatric Patients Undergoing Procedures: A Systematic Review and Meta-Analysis Protocol

Introduction: Many children undergo invasive and noninvasive procedures in the pediatric medical setting, which can be associated with pain and anxiety. Both pharmacological and nonpharmacological interventions are currently being used to minimize pain and anxiety in pediatric patients in the medical setting. Music therapy is an evidence-based, cost-effective, and nonpharmacological service administered by a board-certified music therapist which may reduce pediatric pain and anxiety during procedures. We will undertake a systematic review and meta-analysis that will comprehensively analyze the quantitative publications that detail the effects of music therapy on pediatric pain and anxiety for patients undergoing invasive or noninvasive medical procedures. Method: This protocol is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement and is registered in the International Prospective Register of Systematic Reviews (Prospero; CRD42023474635 ). A three-step, iterative search strategy will include (1) an initial scoping search, (2) development and execution of a comprehensive search strategy, and (3) a search for additional publications. Procedures for disagreements among reviewers and the article screening process are outlined. Statistical analyses including analysis of outcomes and sensitivity analysis has been determined. A plan for critical appraisal of sources using the Grading Recommendations Assessment, Development, and Evaluation (GRADE) and quality of evidence using the Outcome Reporting Bias in Trials (ORBIT) of evidence is also present in this protocol. Conclusion: This protocol is presented with the intention to execute and publish results of this systematic review and meta-analysis to help inform clinical practice and future research.

Harnessing the potential of deep eutectic solvents in biocatalysis: design strategies using CO2 to formate reduction as a case study.

Deep eutectic solvents (DESs) have emerged as green solvents with versatile applications, demonstrating significant potential in biocatalysis. They often increase the solubility of poorly water-soluble substrates, serve as smart co-substrates, modulate enzyme stereoselectivity, and potentially improve enzyme activity and stability. Despite these advantages, screening for an optimal DES and determining the appropriate water content for a given biocatalytic reaction remains a complex and time-consuming process, posing a significant challenge. This paper discusses the rational design of DES tailored to a given biocatalytic system through a combination of experimental screening and computational tools, guided by performance targets defined by solvent properties and process constraints. The efficacy of this approach is demonstrated by the reduction of CO2 to formate catalyzed by NADH-dependent formate dehydrogenase (FDH). By systematically analyzing FDH activity and stability, NADH stability (both long-term and short-term stability after solvent saturation with CO2), and CO2 solubility in initially selected glycerol-based DESs, we were able to skillfully guide the DES screening process. Considering trade-offs between experimentally determined performance metrics of DESs, 20% solution of choline chloride:glycerol in phosphate buffer (ChCl:Gly80%B) was identified as the most promising solvent system for a given reaction. Using ChCl:Gly as a co-solvent resulted in an almost 15-fold increase in FDH half-life compared to the reference buffer and stabilized the coenzyme after the addition of CO2. Moreover, the 20% addition of ChCl:Gly to the buffer improved the volumetric productivity of FDH-catalyzed CO2 reduction in a batch system compared to the reference buffer. The exceptional stability of the enzyme in this co-solvent system shows great potential for application in continuous operation, which can significantly improve process productivity. Additionally, based on easily measurable physicochemical solvent properties and molecular descriptors derived from COSMO-RS, QSAR models were developed, which successfully predicted enzyme activity and stability, as well as coenzyme stability in selected solvent systems with DESs.

Enhancing late postmortem interval prediction: a pilot study integrating proteomics and machine learning to distinguish human bone remains over 15 years

BackgroundDetermining the postmortem interval (PMI) accurately remains a significant challenge in forensic sciences, especially for intervals greater than 5 years (late PMI). Traditional methods often fail due to the extensive degradation of soft tissues, necessitating reliance on bone material examinations. The precision in estimating PMIs diminishes with time, particularly for intervals between 1 and 5 years, dropping to about 50% accuracy. This study aims to address this issue by identifying key protein biomarkers through proteomics and machine learning, ultimately enhancing the accuracy of PMI estimation for intervals exceeding 15 years.MethodsProteomic analysis was conducted using LC–MS/MS on skeletal remains, specifically focusing on the tibia and ribs. Protein identification was performed using two strategies: a tryptic-specific search and a semitryptic search, the latter being particularly beneficial in cases of natural protein degradation. The Random Forest algorithm was used to model protein abundance data, enabling the prediction of PMI. A thorough screening process, combining importance scores and SHAP values, was employed to identify the most informative proteins for model’s training and accuracy.ResultsA minimal set of three biomarkers—K1C13, PGS1, and CO3A1—was identified, significantly improving the prediction accuracy between PMIs of 15 and 20 years. The model, based on protein abundance data from semitryptic peptides in tibia samples, achieved sustained 100% accuracy across 100 iterations. In contrast, non-supervised methods like PCA and MCA did not yield comparable results. Additionally, the use of semitryptic peptides outperformed tryptic peptides, particularly in tibia proteomes, suggesting their potential reliability in late PMI prediction.ConclusionsDespite limitations such as sample size and PMI range, this study demonstrates the feasibility of combining proteomics and machine learning for accurate late PMI predictions. Future research should focus on broader PMI ranges and various bone types to further refine and standardize forensic proteomic methodologies for PMI estimation.

Working conditions of commercial drivers: a scoping review of psychosocial work factors, health outcomes, and interventions

BackgroundPsychosocial work factors significantly influence both organisational and worker health. Poor management of these factors can create precarious working conditions, risking drivers’ health. This review maps evidence on the health impact of these factors and health interventions targeting the working conditions and unhealthy habits of commercial drivers.MethodThe search was conducted in four main databases (PubMed, Central, JSTOR and Dimensions Ai) and other sources like Google Scholar. In All, 28,039 articles were retrieved and through a rigorous screening process, 68 records were included in this scoping review.ResultsThis review found that drivers work in precarious conditions like long driving hours, low job resources and social support, low job control, poor remuneration, workplace abuse and sexual harassment, work-family conflict, lone driving hour, irregular shift work, lack or insufficient breaks during work hours and difficult access to social protection and sanitation facilities. These precarious working conditions may expose drivers to mental health issues, cardiovascular diseases, HIV/AIDs, stroke, chronic fatigue, kidney and bladder issues and musculoskeletal pains. Most health promotion interventions target behaviour at the individual level, such as the benefits of a healthy diet and exercise, with little effort to improving working conditions.ConclusionEmployers in the road transport sector need to implement health promotion interventions that focus on drivers’ well-being. Additionally, improving working conditions and enforcing occupational health and safety standards in the road transport sector are essential for creating a safe and healthy workplace for all commercial drivers.

Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates

BackgroundChest radiographs (CXRs) are widely used to screen for infectious diseases like tuberculosis and COVID-19 among migrants. At such high-volume settings, manual CXR reporting is challenging and integrating artificial intelligence (AI) algorithms into the workflow help to rule out normal findings in minutes, allowing radiologists to focus on abnormal cases. MethodsIn this post-deployment study, all the CXRs acquired during the visa screening process across 33 centers in United Arab Emirates from January 2021 to June 2022 (18 months) were included. The qXR v2.1 chest X-ray interpretation software was used to classify the scans into normal and abnormal, and its agreement against radiologist was evaluated. Additionally, a digital survey was conducted among 20 healthcare professionals with prior AI experience to understand real-world implementation challenges and impact. ResultsThe analysis of 1309,443 CXRs from 1309,431 patients (median age: 35 years; IQR [29–42]; 1030,071 males [78.7 %]) in this study revealed a Negative Predictive Value (NPV) of 99.92 % (95 % CI: 99.92, 99.93), Positive Predictive Value (PPV) of 5.06 % (95 % CI: 4.99, 5.13) and overall percent agreement of the AI with radiologists of 72.90 % (95 % CI: 72.82, 72.98). In the survey, majority (88.2 %) of the radiologists agreed to turnaround time reduction after AI integration, while 82 % suggested that the AI improved their diagnostic accuracy. DiscussionIn contrast with the existing studies, this research uses a substantially large data. A high NPV and satisfactory agreement with human readers indicate that AI can reliably identify normal CXRs, making it suitable for routine applications.