Critical Risk Research Articles

TEMPORARY REMOVAL: Implications of maxillary expansion for the craniofacial phenotype in pediatric and adolescent obstructive sleep apnea

Obstructive Sleep Apnea (OSA) in growing children and adolescents is a multifaceted condition marked by recurrent episodes of breathing interruptions during sleep due to upper airway collapsibility. The pathophysiology of pediatric OSA includes neuromotor dysfunction, inflammation, and anatomical factors related to soft tissues and craniofacial morphology. The prevalence of OSA in children varies between 1-10%, and its impact can extend to behavioral, neurocognitive, cardiovascular, and metabolic issues. Adolescence poses unique diagnostic challenges due to significant physical and hormonal changes, necessitating age-appropriate criteria for OSA assessment.Maxillary transverse deficiency (MTD) is recognized as a critical craniofacial risk factor for pediatric OSA. Rapid Maxillary Expansion (RME) has garnered attention for its ability to open the midpalatal suture, expand the nasal and oropharyngeal airways, and reduce nasal resistance, thereby improving respiratory function. While RME is effective in many cases, microimplant-assisted RME is a promising alternative for severe cases or when conventional RME is insufficient. This technique offers enhanced skeletal expansion and airway improvement, though further research is needed to validate its functional benefits.Case reports highlight the successful application of RME and microimplant-assisted RME in treating pediatric OSA. These interventions resulted in significant improvements in airway dimensions, reduction in apnea-hypopnea index (AHI), and alleviation of symptoms such as snoring and daytime sleepiness.

The SEER Program's evolution: supporting clinically meaningful population-level research.

Although the Surveillance, Epidemiology, and End Results (SEER) Program has maintained high standards of quality and completeness, the traditional data captured through population-based cancer surveillance are no longer sufficient to understand the impact of cancer and its outcomes. Therefore, in recent years, the SEER Program has expanded the population it covers and enhanced the types of data that are being collected. Traditionally, surveillance systems collected data characterizing the patient and their cancer at the time of diagnosis, as well as limited information on the initial course of therapy. SEER performs active follow-up on cancer patients from diagnosis until death, ascertaining critical information on mortality and survival over time. With the growth of precision oncology and rapid development and dissemination of new diagnostics and treatments, the limited data that registries have traditionally captured around the time of diagnosis-although useful for characterizing the cancer-are insufficient for understanding why similar patients may have different outcomes. The molecular composition of the tumor and genetic factors such as BRCA status affect the patient's treatment response and outcomes. Capturing and stratifying by these critical risk factors are essential if we are to understand differences in outcomes among patients who may be demographically similar, have the same cancer, be diagnosed at the same stage, and receive the same treatment. In addition to the tumor characteristics, it is essential to understand all the therapies that a patient receives over time, not only for the initial treatment period but also if the cancer recurs or progresses. Capturing this subsequent therapy is critical not only for research but also to help patients understand their risk at the time of therapeutic decision making. This article serves as an introduction and foundation for a JNCI Monograph with specific articles focusing on innovative new methods and processes implemented or under development for the SEER Program. The following sections describe the need to evaluate the SEER Program and provide a summary or introduction of those key enhancements that have been or are in the process of being implemented for SEER.

Gingipain from Porphyromonas gingivalis causes insulin resistance by degrading insulin receptors through direct proteolytic effects

Periodontitis is a critical risk factor for the occurrence and development of diabetes. Porphyromonas gingivalis may participate in insulin resistance (IR) caused by periodontal inflammation, but the functional role and specific mechanisms of P. gingivalis in IR remain unclear. In the present study, clinical samples were analysed to determine the statistical correlation between P. gingivalis and IR occurrence. Through culturing of hepatocytes, myocytes, and adipocytes, and feeding mice P. gingivalis orally, the functional correlation between P. gingivalis and IR occurrence was further studied both in vitro and in vivo. Clinical data suggested that the amount of P. gingivalis isolated was correlated with the Homeostatic Model Assessment for IR score. In vitro studies suggested that coculture with P. gingivalis decreased glucose uptake and insulin receptor (INSR) protein expression in hepatocytes, myocytes, and adipocytes. Mice fed P. gingivalis tended to undergo IR. P. gingivalis was detectable in the liver, skeletal muscle, and adipose tissue of experimental mice. The distribution sites of gingipain coincided with the downregulation of INSR. Gingipain proteolysed the functional insulin-binding region of INSR. Coculture with P. gingivalis significantly decreased the INSR–insulin binding ability. Knocking out gingipain from P. gingivalis alleviated the negative effects of P. gingivalis on IR in vivo. Taken together, these findings indicate that distantly migrated P. gingivalis may directly proteolytically degrade INSR through gingipain, thereby leading to IR. The results provide a new strategy for preventing diabetes by targeting periodontal pathogens and provide new ideas for exploring novel mechanisms by which periodontal inflammation affects the systemic metabolic state.

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

Abstract Alzheimer’s disease (AD), a widely studied neurodegenerative disorder, poses significant research challenges due to its high prevalence and complex etiology. Age, a critical risk factor for AD, is typically assessed by comparing physiological and estimated brain ages. This study utilizes mouse models expressing human alleles of APOE and human nitric oxide synthase 2 (hNOS2), replicating genetic risks for AD alongside a human-like immune response. We developed a multivariate model that incorporates brain structural connectomes, APOE genotypes, demographic traits (age and sex), environmental factors such as diet, and behavioral data to estimate brain age. Our methodology employs a Feature Attention Graph Neural Network (FAGNN) to integrate these diverse datasets. Behavioral data are processed using a 2D convolutional neural network (CNN), demographic traits via a 1D CNN, and brain connectomes through a graph neural network equipped with a quadrant attention module that accentuates critical neural connections. The FAGNN model demonstrated a mean absolute error in age prediction of 31.85 days and a root mean squared error of 41.84 days, significantly outperforming simpler models. Our analysis further focused on the brain age delta, which assesses accelerated or delayed aging by comparing brain age, predicted by FAGNN, to the chronological age. A high-fat diet and the presence of the human NOS2 gene were identified as significant accelerators of brain aging in the old age group. Key neural connections identified by FAGNN, such as those between the cingulum, corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex, were found to be significant in the aging process. Validation using diffusion MRI-based metrics, including fractional anisotropy and return-to-origin probability measures across these connections, revealed significant age-related differences. These findings suggest that white matter degradation in the connections highlighted by FAGNN plays a key role in aging. Our findings suggest that the complex interplay of APOE genotype with sex, immunity, and environmental factors modulates brain aging and enhance our understanding of AD risk in mouse models of aging.

Critical Risk Factors Associated with Schedule Delays in Gas Processing Facility Projects: Case Study in Indonesia

Critical Risk Factors Associated with Schedule Delays in Gas Processing Facility Projects: Case Study in Indonesia

Modelling bridge deterioration using long short-term memory neural networks: a deep learning-based approach

PurposeBridge deterioration is a critical risk to public safety, which mandates regular inspection and maintenance to ensure sustainable transport services. Many models have been developed to aid in understanding deterioration patterns and in planning maintenance actions and fund allocation. This study aims at developing a deep-learning model to predict the deterioration of concrete bridge decks.Design/methodology/approachThree long short-term memory (LSTM) models are formulated to predict the condition rating of bridge decks, namely vanilla LSTM (vLSTM), stacked LSTM (sLSTM), and convolutional neural networks combined with LSTM (CNN-LSTM). The models are developed by utilising the National Bridge Inventory (NBI) datasets spanning from 2001 to 2019 to predict the deck condition ratings in 2021.FindingsResults reveal that all three models have accuracies of 90% and above, with mean squared errors (MSE) between 0.81 and 0.103. Moreover, CNN-LSTM has the best performance, achieving an accuracy of 93%, coefficient of correlation of 0.91, R2 value of 0.83, and MSE of 0.081.Research limitations/implicationsThe study used the NBI bridge inventory databases to develop the bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.Originality/valueThis study provides a detailed and extensive data cleansing process to address the shortcomings in the NBI database. This research presents a framework for implementing artificial intelligence-based models to enhance maintenance planning and a guideline for utilising the NBI or other bridge inventory databases to develop accurate bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

Development of modified fuzzy FMEA method in environmental risk assessment of earth dams

The investigations have shown that the construction of the dam and its related facilities have significant physical–chemical and ecological effects on the ecosystem. Failure Mode and Effects Analysis (FMEA) is a technique for ranking risks in projects to construct dams, but it has many deficiencies and ambiguities. Therefore, to prevent the shortcomings of the classical method, the modified fuzzy inference system (MFIS-FMEA) method has been used by creating a two-stage model to more accurately assess the risk of Eyvashan Dam. First, all the considered indicators are weighted using the Shannon entropy method, and the environmental risk is prioritized using the Fuzzy OWA method. In this study, two-stage fuzzy reasoning and a Max–Min combination rule are used. When severity (SEV) and occurrence (OCC) variables are combined, the critical risk index (RCI) values are predicted in the first stage. RCI and detection index (DET) input are then used to predict the MFIS-RPN in the second stage. The results of the risk priority number (RPN) in the MFIS-RPN method are much more accurate and serious than the FIS-RPN method due to the two-stage nature and the use of new language terms. The results of the proposed MFIS-RPN technique show that the highest RPN was obtained with immediate action in the dam construction phase for soil erosion and soil pollution and in the dam operation phase for aquatic and water pollution. Therefore, due to the increase in risk score, it is necessary to take immediate and more accurate monitoring during the construction and operation phases.

Updated criteria for the approval of subsequent pregnancy after cesarean section with a transverse uterine fundal incision based on 17 years of experience.

In the case of placenta previa-accreta when the placenta covers the entire anterior uterine wall, it is difficult to avoid transecting the placenta by traditional low-transverse cesarean section (CS), resulting in catastrophic hemorrhage and fetal anemia. To prevent this critical risk, we developed the CS with transverse uterine fundal incision (TUFI) and this technique has been widely used as a beneficial surgical method in clinical practice owing to its safety advantages for the mother and neonate since our first report. However, the risk of uterine rupture during a subsequent pregnancy remains unclear. Based on our 17 years of experience, patients who require TUFI do not need to avoid this beneficial operative method simply because of their desire to conceive again, as long as certain conditions can be met. To approve a post-TUFI pregnancy, an appropriate suture method, delay in conception for at least 12 months with evaluation of the TUFI scar, and cautious postoperative management are at a minimum essential. In this article, we showed our recommendation for operative procedure and discuss the current status of the management of post-TUFI pregnancies based on the evaluation of the TUFI wound scar and experience with postoperative pregnancies.

Early-life risk factors for depression among young adults in the United States general population: Attributable risks and gender differences

BackgroundThis study adopts individual and societal-level approaches to examine the contribution of childhood risk factors to major depressive episodes (MDE) in 2526 American young adults. MethodsNationally representative data from the 2017 U.S. Panel Study of Income Dynamics – Transition into Adulthood Supplement (PSID-TAS) were analyzed using multivariate methods to assess the impact of parental mental illness, childhood adversities, childhood mental disorders, and childhood physical conditions. Adjusted odds ratios and population attributable risk proportions (PARPs) are calculated to estimate the proportion of MDE cases related to risk factors. ResultsThe 12-month prevalence of positive screens for MDE was 25.4 %. Approximately 34 % of these were attributable to childhood mental disorders, 24 % to childhood physical conditions, 21 % to childhood adversities, and 16 % to parental mental illness. Childhood and parental depression were critical risk factors, both at the individual (odds ratio exceeding 2) and societal (PARP approximately 24 %) levels. Gender-specific risk factors were identified, with childhood physical abuse and childhood anxiety disorders constituting risk factors for females, and childhood externalizing disorders and childhood headaches as risk factors for males. Approximately 60 % of U.S. young adult MDE cases are attributable to risk factors before age 18. LimitationsPossible over reporting of MDE may have biased the associations between predictors and depression. ConclusionsExposure to depression at a young age—one's own or parental depression—is a robust risk factor for both genders. Policies and interventions focused at alleviating the societal burden of depression should value its generational transmission.

Assessment of Risk Factors in Condominium Buildings Construction in Metro Manila using Analytic Hierarchy Process (AHP)

The rapid urbanization of Metro Manila led to a surge in condominium construction projects, which, despite offering efficient housing solutions, faced significant challenges due to dense urban environment, susceptibility to natural disasters & the health and safety issues related to accidents, losses, injuries, damages, and/or delays arising during the execution of a project which may adversely affect its success. This study explored the efficacy of the Analytic Hierarchy Process (AHP) in assessing and prioritizing risks specific to Metro Manila's condominium construction projects, addressing a critical gap in current risk assessment methodologies. The study engaged mixed methods, integrating Meta-Analysis, Experts Consultation, and AHP, to identify and prioritize Risk Factors (RFs) in condominium construction projects. A systematic review through meta-analysis revealed nine critical RFs, including Technical/Design, Financial/Economic, Environmental, Regulatory/Legal, Socio-Political, Logistics/Supply Chain, Management/Supervision, Quality, and Health and Safety Risks. Inputs from experts from a diverse group of qualified professionals had been pooled to validate these findings. Through AHP analysis, risks specific to Metro Manila's condominium construction projects were revealed, with Health and Safety Risk (19.52%), Quality Risk (15.97%) and Management/Supervision Risk (15.77%) as the major concerns. An internal consistency of 0.71% attested to the reliability testing of the results. Given the complexity of condominium construction projects, this study responds to an important demand: a more systemic understanding of risk factors. Moreover, this study is relevant to further enhance and improve sustainability in condominium projects as well as performance of the construction industry.

Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors

BackgroundDiabetic peripheral neuropathy (DPN) and lower extremity arterial disease (LEAD) are significant contributors to diabetic foot ulcers (DFUs), which severely affect patients’ quality of life. This study aimed to develop machine learning (ML) predictive models for DPN and LEAD and to identify both shared and distinct risk factors.MethodsThis retrospective study included 479 diabetic inpatients, of whom 215 were diagnosed with DPN and 69 with LEAD. Clinical data and laboratory results were collected for each patient. Feature selection was performed using three methods: mutual information (MI), random forest recursive feature elimination (RF-RFE), and the Boruta algorithm to identify the most important features. Predictive models were developed using logistic regression (LR), random forest (RF), and eXtreme Gradient Boosting (XGBoost), with particle swarm optimization (PSO) used to optimize their hyperparameters. The SHapley Additive exPlanation (SHAP) method was applied to determine the importance of risk factors in the top-performing models.ResultsFor diagnosing DPN, the XGBoost model was most effective, achieving a recall of 83.7%, specificity of 86.8%, accuracy of 85.4%, and an F1 score of 83.7%. On the other hand, the RF model excelled in diagnosing LEAD, with a recall of 85.7%, specificity of 92.9%, accuracy of 91.9%, and an F1 score of 82.8%. SHAP analysis revealed top five critical risk factors shared by DPN and LEAD, including increased urinary albumin-to-creatinine ratio (UACR), glycosylated hemoglobin (HbA1c), serum creatinine (Scr), older age, and carotid stenosis. Additionally, distinct risk factors were pinpointed: decreased serum albumin and lower lymphocyte count were linked to DPN, while elevated neutrophil-to-lymphocyte ratio (NLR) and higher D-dimer levels were associated with LEAD.ConclusionsThis study demonstrated the effectiveness of ML models in predicting DPN and LEAD in diabetic patients and identified significant risk factors. Focusing on shared risk factors may greatly reduce the prevalence of both conditions, thereby mitigating the risk of developing DFUs.

Security and Privacy Challenges in Software as a Service (SaaS)

This document In the rapidly evolving landscape of cloud computing, Software as a Service (SaaS) has emerged as a dominant model for delivering software applications over the internet. However, this model introduces significant security and privacy challenges. This research paper delves into these issues, highlighting the unique risks associated with SaaS platforms. The study explores common vulnerabilities such as data breaches, unauthorized access, data loss, and identity theft, which are exacerbated by the centralized nature of SaaS solutions where data is stored on remote servers managed by service providers. To address these challenges, the paper underscores the importance of robust security measures, including encryption, access controls, and secure software development practices. Additionally, it emphasizes the necessity for SaaS providers to adopt advanced privacy protection techniques like data anonymization and differential privacy to meet user expectations and comply with stringent legal regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Through a comprehensive review of literature and analysis of current SaaS security practices, the research identifies critical risks and provides strategic recommendations for enhancing security and privacy in SaaS environments. By integrating multiple layers of security throughout the software development lifecycle, enforcing strict access management protocols, and ensuring compliance with regulatory standards, SaaS providers can significantly mitigate risks and safeguard sensitive user data. The findings of this study highlight the imperative for continuous improvement in SaaS security strategies to keep pace with emerging threats and technological advancements. Ultimately, by prioritizing security and privacy, SaaS providers can not only protect their users but also gain a competitive edge in the increasingly crowded market of cloud services.

Precursors of falls from height in two main contractor companies in the Chilean construction sector and their subcontractors: Shared vision and preventive prioritization.

Risk factors that can result in serious injuries or deaths are frequently not understood or prevented because of the traditional way in which organizations approach safety. This is especially true for falls from heights in the construction sector, which remain a leading safety hazard in construction globally. A theoretical framework derived from a thorough review of international evidence provided the foundation for the conduct of 50 structured focus groups with a total of 209 participants representing construction workers and supervisors in two main Chilean construction contractor companies and their subcontractors. A total of 627 precursors (risk factors), 56 taxonomies (work tasks/activities), and 622 defenses (prevention actions) were identified. These were classified in three macro dimensions and 13 subdimensions in total, providing a comprehensive framework for the origin, manifestation, and neutralization of such precursors. The findings provide a useful framework for understanding of the most critical risks to which workers are exposed and actions to prevent them. This framework should form the basis for the development of practical safety programs and tools such as management and training checklists, and data analysis for early warning signs.

Predictors of hip fracture in 15 European countries: a longitudinal study of 48,533 geriatric adults using SHARE dataset.

Hip fracture is a major cause of functional disability, mortality, and health costs. However, the identification and characterization of its causative factors remain poor. We investigated demography, handgrip strength (HGS), depression, and multiple age-associated comorbidities for predicting future hip fracture in individuals aged 50 or above from 15 European countries (n = 48,533). All participants were evaluated from 2013 to 2020 using four successive waves of the Survey of Health, Aging, and Retirement in Europe (SHARE). Altogether, 1130 participants developed hip fractures during the study period. We identified female gender, an advancing age from quinquagenarians onward, and a poor socioeconomic status as critical risk factors for future hip fracture. Having mobility difficulty, a low HGS (< 27kg in men, < 16kg in women) and higher scores on Euro-D depression scales were also significant risk factors for hip fracture. Summated scales of hypertension, diabetes mellitus, cancer, Alzheimer's disease, and stroke did not appear as risk factors. Collectively, we report advancing age, female gender, low HGS, and depression as independent risk factors for hip fracture. Our findings are useful in identifying high-risk populations for hip fractures in pre-clinical settings before rigorous evaluation and treatment in clinics.

Understanding no fault found event risk in military aircraft MRO planning: A step towards robust bidding and contract finalization

No fault found (NFF) is a reported fault for which the root cause cannot be found. An NFF event not only reduces the availability of the equipment to customers but also results in financial losses to the manufacturer. To date, risk due to NFF events is not considered separately in aircraft Maintenance, Repair, and Overhaul (MRO) planning. In this paper, we have studied the NFF event risk identification, distribution, and optimization in an aircraft MRO process based on data collected over three decades of aircraft maintenance. For framework development and formulating the critical NFF risk drivers, this paper applies soft system methodology with detailed semi-structured interviews of industry experts. For NFF risk distribution and optimization, we have used agent-based simulation modeling. The quantitative assessment of more than 5000 NFF cases in an aircraft MRO organization shows that NFF events are significantly non-deterministic during the life cycle of an aircraft. The three identified critical NFF risk drivers in the research are the MRO process stage, aircraft life cycle stage, and human working skills. The MRO process stage is the time of the NFF event during repair activity. The life cycle stage represents the timeline of the NFF event in useful aircraft life. Human working skill represents the role of manpower in the NFF event solution. Further theoretical elaborations of the research lead to the optimized NFF risk break-up at the contract preparation stage and risk driver's sensitivity analysis by the aircraft assembler, operator, and sub-contractor at the commercial bid submission stage.

Analysis of High-Risk Factors and Mortality Prediction of Ruptured Abdominal Aortic Aneurysm

Ruptured abdominal aortic aneurysms (RAAAs) are among the most dangerous emergencies in vascular surgery, with a high death rate and numerous risk factors influencing perioperative death. Therefore, identifying the critical risk factors for RAAAs is crucial to increasing their survival rate. Our aim was to identify those risk factors from a wide range of parameters. Retrospective analysis of hospitalised RAAA patients treated at this center between May 2004 and January 2023. After comparing the preoperative data of patients who survived and those who died, high-risk characteristics influencing the perioperative care of RAAA patients were identified, and logistic regression analysis was carried out. The mean follow-up time was 45.34 months. During the study period, a total of 155 patients (average age 67.4±71.93 years, 123 (78.85%)males, 32 (20.51%)females) were enrolled. The patients participating in the group were divided into survival group (n = 123) and death group (n = 27). The main differences included hemodynamic instability (51.9% vs 28.5%; P=0.019), sudden cardiac arrest (14.8% vs 1.6%; P=0.010), deterioration of consciousness (40.7% vs 17.1%; P=0.007), renal impairment (22.2% vs 2.4%; P=0.001), chronic kidney disease (18.5% vs3.2%; P=0.010). There is also a history of cancer (Ca) (18.5% vs 4.1%; P=0.021). Risk factors for Endovascular aneurysm repair (EVAR) include diastolic blood pressure ≤ 50 mmHg (36.4% vs 8.0%; P=0.025), renal function impairment (18.2% vs 0; P=0.015), and chronic kidney disease (27.3% vs 4.0%; P=0.028). Risk factors for open surgical repair (OSR) include diastolic blood pressure ≤ 50 mmHg (40.0% vs 6.3%; P=0.014). Finally, the above statistically significant factors were analyzed by Logistic regression analysis, and it was found that diastolic blood pressure ≤ 50mmHg, cardiac arrest, renal function damage and Ca history were independent risk factors. We followed 123 individuals and 14 were lost to follow-up, with an overall survival rate of 43.8%. Hemodynamics, which includes shock, blood pressure, cardiac arrest, deterioration of consciousness, and other conditions, are the primary risk factors for the perioperative death of a ruptured abdominal aortic aneurysm. Simultaneously, diastolic blood pressure ≤50mmHg was found to be associated with risk factors for OSR, whereas renal function impairment, chronic renal illness, and diastolic blood pressure ≤50mmHg were associated with risk for EVAR.

Surgical systematic reviews: best available evidence or disposable waste?

Abstract Evidence-based medicine demands treatment options for patients to be based on the current best available evidence. Systematic reviews (SRs) with meta-analyses allow surgeons to make therapeutical decisions in accordance with the highest level of evidence. Also, high-quality SRs support physicians to challenge the colossal amount of new research data created daily. The systematic review working group of the Study Center of the German Society of Surgery (SDGC) has created specific methodological literature regarding surgical SRs, giving recommendations to assess critical risk of bias and to prevent the creation of SRs that do not provide any new insights to the field. SRs should only be considered if there is new clinically relevant data available that allows the SR to create novel evidence. To address the dilemma of new SRs generated without adding new evidence, living systematic reviews and evidence mapping represent an innovative approach, in which SRs are regularly updated with new research data.

Relationship of pancreatitis to pancreatic cancer: from population-based observations to preclinical and mechanistic insights

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies, accounting for 95% of pancreatic cancer cases, with a 5-year survival rate of around 10%. The relationship between pancreatitis and pancreatic cancer has been noted within the medical community. Recent epidemiological data and findings from experimental mouse models have underscored pancreatitis as a critical risk factor for pancreatic cancer. Therefore, exploring the mechanisms underlying the transition from pancreatitis to pancreatic cancer is crucial for improving early detection and treatment strategies for pancreatic cancer. In this review, we conducted a comprehensive search of the PubMed database and discussed relevant original studies, focusing on three key areas: findings from population- and animal-based studies, the role of pancreatic epithelial cell-intrinsic factors, and the impact of immune cells and cytokines. Additionally, we offered our prospectives on possible future research directions.

High Prevalence of Antibiotic Resistance in Traditionally Fermented Foods as a Critical Risk Factor for Host Gut Antibiotic Resistome.

This study aimed to assess the suitability of fermented food interventions to replenish damaged gut microbiota. Metagenomic assessment of published sequencing data found that fermented food interventions led to a significant increase in the gut antibiotic resistome in healthy human subjects. Antibiotic resistome and viable antibiotic-resistant (AR) bacteria were further highly prevalent in retail kimchi and artisan cheeses by metagenomic and culture analyses. Representative AR pathogens of importance in nosocomial infections, such as Klebsiella pneumoniae, Serratia marcescens, and vancomycin-resistant Enterococcus (VRE), as well as commensals and lactic acid bacteria, were characterized; some exhibited an extremely high minimum inhibitory concentration (MIC) against antibiotics of clinical significance. Exposing fermented food microbiota to representative antibiotics further led to a boost of the corresponding antibiotic and multidrug-resistance gene pools, as well as disturbed microbiota, including the rise of previously undetectable pathogens. These results revealed an underestimated public health risk associated with fermented food intervention at the current stage, particularly for susceptible populations with compromised gut integrity and immune functions seeking gut microbiota rescue. The findings call for productive intervention of foodborne AR via technology innovation and strategic movements to mitigate unnecessary, massive damages to the host gut microbiota due to orally administered or biliary excreted antibiotics.

Loop-mediated isothermal amplification (LAMP) assay for early on-site detection of Group B Streptococcus infection in neonatal sepsis blood sample.

Neonatal sepsis, often attributed to Group B Streptococcus (GBS) infection, poses a critical health risk to infants, demanding rapid and accurate diagnostic approaches. Existing diagnostic approaches are dependent on traditional culture methods, a process that requires substantial time and has the potential to delay crucial therapeutic assessments. This study introduces an innovative Loop-Mediated Isothermal Amplification (LAMP) assay for the early on-site detection of GBS infection from neonatal sepsis blood samples. To develop a LAMP assay, the primers are designed for the selective targeting of a highly conserved segment within the cfb gene encoding the CAMP factor in Streptococcus agalactiae ensuring high specificity. Rigorous optimization of reaction conditions, including temperature and incubation time, enhances the efficiency of the LAMP assay, enabling rapid and reliable GBS detection within a short timeframe. The diagnostic efficacy of the LAMP assay was evaluated using spiked blood samples by eliminating the DNA extraction step. The simplified colorimetric LAMP assay has the capability to detect S. agalactiae in a neonatal blood sample containing 2 CFU/mL during sepsis. Additionally, the LAMP assay effectively detected S. agalactiae in both the standard and spiked blood samples, with no detectable interference with blood. This optimised LAMP assay emerges as a promising tool for early GBS detection, offering a rapid and accurate on-site solution that has the potential to inform timely interventions and improve outcomes in neonatal sepsis cases.