Cardiometabolic Morbidity (Obesity and Hypertension) in PTSD: A Preliminary Investigation of the Validity of Two Structures of the Impact of Event Scale-Revised.

Background: Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke maladaptive behaviors (e.g., compulsive buying, disordered eating, and an unhealthy lifestyle), resulting in adverse cardiometabolic events (e.g., hypertension and obesity), which may implicate the treatment of this complex condition. The diagnostic criteria for PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Including additional symptoms such as emotional numbing, sleep disturbance, and irritability strengthens the representation of the Impact of Event Scale-Revised (IES-R), suggesting that models with four, five, or six dimensions better capture its structure compared to the original three-dimensional model. Methods: Using a convenience sample of 58 Russian dental healthcare workers (HCWs: mean age = 44.1 ± 12.2 years, 82.8% females), this instrumental study examined the convergent, concurrent, and criterion validity of two IES-R structures: IES-R3 and IES-R6. Results: Exploratory factor analysis uncovered five factors, which explained 76.0% of the variance in the IES-R. Subscales of the IES-R3 and the IES-R6 expressed good internal consistency (coefficient alpha range = 0.69-0.88), high convergent validity (item total correlations r range = 0.39-0.81, and correlations with the IES-R's total score r range = 0.62-0.92), excellent concurrent validity through strong correlations with the PTSD Symptom Scale-Self Report (PSS-SR: r range = 0.42-0.69), while their criterion validity was indicated by moderate-to-low correlations with high body mass index (BMI: r range = 0.12-0.39) and the diagnosis of hypertension (r range = 0.12-0.30). In the receiver-operating characteristic (ROC) curve analysis, all IES-R models were perfectly associated with the PSS-SR (all areas under the curve (AUCs) > 0.9, p values < 0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale were significantly associated with high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, were significantly associated with hypertension. In the two-step cluster analysis, five sets of all trauma variables (IES-R3/IES-R6, PSS-SR) classified the participants into two clusters according to their BMI (normal weight/low BMI vs. overweight/obese). Meanwhile, only the IES-R, PSS-SR, and IES-R3 dimensions successfully classified participants as having either normal blood pressure or hypertension. Participants in the overweight/obese and hypertensive clusters displayed considerably higher levels of most trauma symptoms. Input variables with the highest predictor importance in the cluster analysis were those variables expressing significant associations in correlations and ROC analyses. However, neither IES-R3 nor IES-R6 contributed to BMI or hypertension either directly or indirectly in the path analysis. Meanwhile, age significantly predicted both health conditions and current smoking. Irritability and numbing were the only IES-R dimensions that significantly contributed to current smoking. Conclusions: The findings emphasize the need for assessing the way through which various PTSD symptoms may implicate cardiometabolic dysfunctions and their risk factors (e.g., smoking and the intake of unhealthy foods) as well as the application of targeted dietary and exercise interventions to lower physical morbidity in PTSD patients. However, the internal and external validity of our tests may be questionable due to the low power of our sample size. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts, is pivotal to validate the results (e.g., in specific groups, such as those with confirmed traumatic exposure and comorbid mood dysfunction).

Oral Disease and Atherosclerosis May Be Associated with Overlapping Metabolic Pathways.

OBJECTIVES: Dental caries and periodontitis are among the most prevalent chronic diseases worldwide and have been associated with atherosclerotic cardiovascular diseases (ASCVD). This study aimed to determine (1) the independent associations between subclinical ASCVD markers (carotid intima media thickness [CIMT] and coronary artery calcification [CAC]) and quantitative indices of oral disease including the decayed, missing, and filled teeth (DMFT) index, gingivitis parameters, periodontal status, and number of teeth lost and (2) the extent to which metabolites altered in individuals with oral disease overlapped with those altered in individuals with ASCVD. METHODS: We used data from 552 participants recruited through the Dental Strategies Concentrating on Risk Evaluation project. Oral examinations were conducted, and CIMT and CAC were measured. Multiple linear regression models were constructed with CIMT and CAC as dependent variables in the epidemiologic analysis. In the metabolomic analysis, logistic or linear regression was used to test 1,228 metabolites for association with each phenotype adjusted for age, sex, race, blood pressure, smoking, diabetes, cholesterol, high-sensitivity C-reactive protein, and interleukin-6. RESULTS: None of the oral disease markers were significant predictors of ASCVD markers in the fully adjusted models. However, critical lipid and lipid-signaling pathway metabolites were significantly associated with gingivitis, periodontitis, and DMFT: the lysophospholipid pathway (odds ratio [OR] = 2.29, false discovery rate [FDR]-adjusted P = 0.038) and arachidonate with gingivitis (OR = 2.35, FDR-adjusted P = 0.015), the sphingolipid metabolism pathway with periodontitis (OR = 2.09, FDR-adjusted P = 0.029), and borderline associations between plasmalogen and lysophospholipid pathways and DMFT (P = 0.055). Further, the same metabolite from the sphingolipid metabolism pathway, sphingomyelin (d17:1/14:0, d16:1/15:0), was inversely associated with both CIMT (ß = -0.14, FDR-adjusted P = 0.014) and gingivitis (OR = 0.04, FDR-adjusted P = 0.033). CONCLUSIONS: The discovery of a common sphingomyelin metabolite in both disease processes is a novel finding suggesting that gingivitis and periodontitis may be associated with some overlapping metabolic pathways associated with ASCVD and indicating potential shared mechanisms among these diseases. KNOWLEDGE TRANSFER STATEMENT: The same metabolites may be altered in atherosclerosis and oral disease. Specifically, a common sphingomyelin metabolite was inversely associated with gingivitis and carotid intima media thickness, a subclinical marker of atherosclerotic cardiovascular disease. These findings can provide valuable insights for future mechanistic studies to establish potential causal relationships, with the hope of influencing disease prevention and targeted early treatment.

Potential of oleaginous microbes for lipid accumulation and renewable energy generation.

Biocomponents (such as lipids) accumulate in oleaginous microorganisms and could be used for renewable energy production. Oleaginous microbes are characterized by their ability to accumulate high levels of lipids, which can be converted into biodiesel. The oleaginous microbes (including microalgae, bacteria, yeast, and fungi) can utilize diverse substrates. Thus, in this study, commercially viable oleaginous microorganisms are comparatively summarized for their growth conditions, substrate utilization, and applications in biotechnological processes. Lipid content is species-dependent, as are culture conditions (such as temperature, pH, nutrients, and culture time) and substrates. Lipid production can be increased by selecting suitable microorganisms and substrates, optimizing environmental conditions, and using genetic engineering techniques. In addition, the emphasis on downstream processes (including harvesting, cell disruption, lipid extraction, and transesterification) highlights their critical role in enhancing cost-effectiveness. Oleaginous microorganisms are potential candidates for lipid biosynthesis and could play a key role in meeting the energy needs of the world in the future.

Complete genome sequence of cephalosporin and tetracycline-resistant Citrobacter freundii CF51 isolate from a patient with urinary tract infection.

We report here the complete genome sequence of cephalosporin (cefazolin and cephalotin) and tetracycline-resistant Citrobacter freundii strain CF51, isolated from a patient with a urinary tract infection. The genome comprises a 5.0-Mb chromosome, along with an 87.2-kb plasmid and a 23.9-kb plasmid.

The Impact of Bariatric Surgery on Weight Reduction and the Resolution of Comorbidities in Older Geriatric Populations of Saudi Arabia: A Retrospective Study.

Background Obesity is a significant health concern among older adults, leading to various comorbidities and reduced quality of life. Bariatric surgery (BS) has emerged as a potential intervention, but its efficacy in geriatric populations, particularly in Saudi Arabia, is not well-established. Aims This retrospective study aims to evaluate the impact of BS on weight reduction and comorbidity resolution in Saudi Arabian geriatric populations. Methods A retrospective cohort study was conducted at King Khalid Hospital, Saudi Arabia, involving geriatric patients aged 60 and above who underwent BS between January 2018 and December 2022. Data were collected from medical records and analyzed using descriptive statistics, chi-square tests, t-tests, and multivariate regression analysis. Results The study included a total of 26 patients with a mean age of 64 years. Of these, 18 (69.3%) were females, while eight (30.7%) were males, and 23 (87%) underwent sleeve gastrectomy (SG), while three (13%) had Roux-en-Y gastric bypass (RYGB). Preoperative comorbidities majorly included diabetes (17, 35.42%), hypertension (11, 22.92%), and anemia (four, 8.33%). The average body mass index (BMI) of the patients decreased significantly from 45.12 to 37.29 at three months and further to 31.36 at six months post surgery. Total weight loss (TWL) was 19.92% at three months and 35.15% at six months, while the percentage of excess weight loss (%EWL) was 33.42% at three months and 57.85% at six months. Results also showed a significant reduction in the number of comorbidities postoperatively. A significant association with gender, preoperative weight, and preoperative height at three and six months and a significant association with preoperative BMI and comorbidity status at six months were recorded. Conclusion The study suggests that bariatric surgery is effective in achieving significant weight loss and improving comorbidities in geriatric patients. Few demographic and clinical features affect the outcome of the weight loss.

Optimizing physicochemical properties, antioxidant potential, and antibacterial activity of dry ginger extract using sonication treatment.

This research paper focused on enhancing the physico-chemical attributes, antioxidant capacity, and antibacterial effectiveness of dry ginger extract through sonication as an assistant extraction treatment. Ginger, resulting from the rhizome of Zingiber officinale Roscoe, is known for its culinary and medicinal uses outstanding to its antioxidant and antimicrobial possessions from phenolic acids and flavonoids. The study explored the use of sonication as an assistant extraction method and found that it significantly augmented the total phenolic content of the ginger extract by 28 % compared to traditional extraction methods, reaching 10.55 ± 1.50 mg GAE/g, DW. The research assessed the physicochemical belongings, antioxidant action, and antibacterial possibility of the sonicated ginger extract. The sonicated extract exhibited scavenging activity against the DPPH radical of 56.0 %. Pearson correlation investigation revealed a strong confident correlation between the phenolic content and antioxidant activity (r = 0.92, p < 0.01), as well as volatile compounds exhibited a moderate confident correlation with antibacterial action (r = 0.67, p < 0.05). The sonicated ginger extract also demonstrated potent antibacterial action, preventing the growth of both Gram-positive and Gram-negative bacteria. These findings contribute to the development of more efficient methods for extracting phenolic from ginger and provide insights into the relationships between phenolic and bioactive properties.

Comprehensive Genomic Analysis of Uropathogenic E. coli: Virulence Factors, Antimicrobial Resistance, and Mobile Genetic Elements.

Our whole-genome sequencing analysis of sixteen uropathogenic E. coli isolates revealed a concerning picture of multidrug resistance and potentially virulent bacteria. All isolates belonged to four distinct clonal groups, with the highly prevalent ST131 lineage being associated with extensive antibiotic resistance and virulence factors. Notably, all isolates exhibited multidrug resistance, with some resistant to as many as 12 antibiotics. Fluoroquinolone resistance stemmed primarily from efflux pumps and mutations in gyrase and topoisomerase genes. Additionally, we identified genes encoding resistance to extended-spectrum cephalosporins, trimethoprim/sulfamethoxazole, and various heavy metals. The presence of diverse plasmids and phages suggests the potential for horizontal gene transfer and the dissemination of virulence factors. All isolates harbored genomic islands containing virulence factors associated with adhesion, biofilm formation, and invasion. Genes essential for iron acquisition, flagella biosynthesis, secretion systems, and toxin production were also prevalent. Adding further complexity to understanding the isolates' genetic makeup, we identified CRISPR-Cas systems. This study underscores the need for continued genomic surveillance in understanding the pathogenic mechanisms and resistance profiles of uropathogenic E. coli to aid in developing targeted therapeutic strategies.

Overcoming the coherence time barrier in quantum machine learning on temporal data.

The practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset operations to reduce circuit executions, while still maintaining an appropriate length persistent temporal memory in the quantum system, confirmed through the proposed Volterra Series analysis. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling in monitored circuits and sampling noise, problems that persist even in hypothetical fault-tolerant quantum computers that have yet to be realized. To validate our approach, we consider the channel equalization task to recover test signal symbols that are subject to a distorting channel. Through simulations and experiments on a 7-qubit quantum processor we demonstrate that NISQRC can recover arbitrarily long test signals, not limited by coherence time.

The interplay between vitamin D status, subclinical inflammation, and prediabetes.

Vitamin D's role extends beyond classical calcium and phosphate homeostasis to encompass a pivotal influence on immune modulation and metabolic health. The mechanisms by which vitamin D exerts these effects involve its conversion to hormonally active calcitriol, which binds intracellular vitamin D receptors, initiating various downstream cascades. In this review, we tease out the evidence showing the relationship between vitamin D deficiency and prediabetes within the context of subclinical inflammation, with a special focus on the novel monocyte-to-HDL ratio (MHR), a novel inflammatory marker reflecting subclinical inflammation. This was based on a thorough literature review using reputable databases covering the period from 1980 to 2024. In light of this, we discuss calcitriol's anti-inflammatory effects and consequently link vitamin D deficiency to both overt and subclinical inflammation. Additionally, the utility of several biomarkers, notably MHR, in investigating this association is also discussed. We further reviewed the role of vitamin D deficiency in precipitating prediabetes and type 2 diabetes mellitus (T2DM) via insulin resistance, decreased insulin synthesis and secretion, and subclinical inflammation. Taken together, this mini review highlights that vitamin D deficiency is significantly associated with subclinical inflammation, playing a critical role in the development of prediabetes and the progression to T2DM. Addressing vitamin D deficiency through appropriate interventions may serve as a preventative measure against the development of prediabetes and T2DM.

Effects of Hormonal Replacement Therapy and Mindfulness-Based Stress Reduction on Climacteric Symptoms Following Risk-Reducing Salpingo-Oophorectomy.

Breast Cancer Associated Susceptibility Proteins Type 1/2 (BRCA1/2) promote cellular functioning by modulating NRF2-mediated antioxidant signaling. Redox failure in women with BRCA1/2 insufficiency increases the risk for breast/ovarian/uterine cancers. Risk-reducing salpingo-oophorectomy (RRSO) is a prophylactic surgery of the reproductive organs, which is frequently conducted by the age of 40 to lower the occurrence of cancer in women with BRCA1/2 mutations. However, abrupt estrogen decline following RRSO causes ovarian failure, which implicates various cellular physiological processes, resulting in the increased release of free radicals and subsequent severe onset of menopausal symptoms. Comfort measures (e.g., hormonal replacement therapy (HRT) and mindfulness-based stress reduction (MBSR)) may improve chronological menopause-related quality of life, but their specific effects are not clear in women with gene mutations. Aiming to fill the gap, this study used path analysis to examine the effects of HRT and MBSR on menopausal symptoms among RRSO patients (N = 199, mean age = 50.5 ± 6.7 years). HRT directly alleviated the levels of urogenital symptoms (ß = -0.195, p = 0.005), which mediated its indirect significant effects on the somatic-vegetative and psychological symptoms of menopause (ß = -0.046, -0.067; both p values = 0.004, respectively), especially in BRCA2 carriers and in women who were currently physically active, premenopausal at the time of RRSO, had a high BMI, and had no history of breast cancer. It increased the severity of urogenital symptoms in women with a history of cancer. MBSR, on the other hand, was associated with indirect increases in the intensity of the somatic-vegetative and psychological symptoms of menopause (ß = 0.108, 0.029; p = 0.003, 0.033, respectively). It exerted positive direct effects on different menopausal symptoms in multigroup analysis. The results suggest that young women undergoing recent RRSO may benefit from HRT at an individual level, while their need for extensive measures to optimize their psychological wellbeing is ongoing. The adverse effects of MBSR, which are captured in the present study, imply that MBSR may interfere with redox sensitivity associated with estradiol fluctuations in BRCA1/2 carriers. Investigations are needed to test this hypothesis and elaborate on the underlying mechanisms in these women.

Evaluation of Sleep Quality Among People Living With Type 2 Diabetes Mellitus in Taif, Saudi Arabia: A Cross-Sectional Study.

BACKGROUND:  Type 2 diabetes mellitus (T2DM) is characterized by high blood glucose levels, which are highly associated with poor sleep quality, cardiovascular disease, and pathological changes. This research examines the relationship between sleep quality and T2DM and compares it with nondiabetics within the Taif community. The findings of this study will provide valuable insights and recommendations to enhance the overall health quality in Taif, Saudi Arabia. METHODOLOGY:  A cross-sectional study was conducted on 547 patients with T2DM between December 1, 2023, and April 1, 2024, in Taif. The sleep quality was assessed using the Sleep Quality Questionnaire (SQQ). Data were collected using an online questionnaire with two parts: primary demographic data and an assessment of sleep quality using the SQQ. RESULTS:  Our study enrolled 814 participants, including 547 with T2DM and 267 nondiabetics. Participants with T2DM had poorer sleep quality, with a median score of 21 vs. 25 (P < 0.001). Significant factors affecting sleep quality included gender (P = 0.002), marital status (P = 0.023), and job status (P = 0.023). Nondiabetics had better sleep quality (76%) than participants with T2DM (61.1%). Males, married, and employed individuals reported higher sleep quality scores. CONCLUSIONS:  Research indicates that individuals with T2DM experience lower sleep quality than the general population, particularly among female, unmarried, and unemployed individuals. To enhance sleep quality in patients with T2DM, it is essential to increase awareness, provide education on proper sleep habits, and highlight the importance of effective diabetes management, screening for sleep disorders, and consistent monitoring.

Exploring Faculty Preparedness for Artificial Intelligence-Driven Dental Education: A Multicentre Study.

Introduction In the modern era, technology, including artificial intelligence (AI), is the centre of digital innovation. AI is revolutionising numerous fields, including the healthcare sector, globally. Incorporating AI in dental education may help in improving the diagnostic accuracy, learners' experiences, and effectiveness of the management of dental education institutions. However, successful implementation of AI requires the faculty's willingness to incorporate it into their practices. Thus, this research aims to explore the readiness of faculty members to integrate AI into dental education. Methodology The study employed a qualitative exploratory design to gather in-depth insights into faculty readiness for AI-driven dental education. Purposive sampling was employed, and 21 faculty members from public and private dental colleges in South Punjab participated in semi-structured interviews. The interviews focused on understanding participants' perceptions, experiences, and challenges related to AI integration in dental education. Thematic analysis was conducted utilising Braun and Clarke's framework to identify key themes and subthemes from the qualitative data using inductive coding. Results Five major themes and 14 subthemes emerged from the data analysis. Faculty members had low AI literacy coupled with diverse perceptions; some participants perceived AI as a solution for revolutionising teaching and learning, while others criticised its misuse as academic misconduct by students, an effect on students' critical thinking, and a threat to conventional jobs. However, most of the respondents also considered AI beneficial for students with remote access or from marginalised populations in terms of accessing and learning from limited resources. Concerns that participants highlighted included a lack of training opportunities, limited facilities, ethical concerns pertaining to data privacy, and assessment bias. Some of the recommendations provided by the respondents include the provision of training opportunities, the allocation of resources and infrastructure, and continuous effective support from institutions for the integration of AI in dental education. Conclusions This study emphasised the readiness of the faculty when it comes to the integration of AI in dental education. The faculty considered AI favourable for digitization and innovative education, although there is a lack of awareness of its application. Regarding the benefits of utilising AI, respondents highlighted its quick response, prediction of students' performance, and flexibility in learning. The challenges included lack of awareness regarding its implementation, inadequate training, lack of availability of resources, lack of institutional support, the problem of data confidentiality, and resistance to change. Suggestions included the provision of technical support, skills training, and the provision of required infrastructure. Participants recommended that AI tools must incorporate cultural and contextually specific content, use technical support for problems, and incorporate constant response systems to improve the AI tools for novice users, especially within developing regions such as Pakistan.

EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.

In recent years, electroencephalography (EEG) has been investigated for identifying brain disorders. This technique involves placing multiple electrodes (channels) on the scalp to measure the brain's activities. This study focuses on accurately detecting mild cognitive impairment (MCI) from the recorded EEG signals. To achieve this, this study first introduced discrete wavelet transform (DWT)-based approaches to generate reliable biomarkers for MCI. These approaches decompose each channel's signal using DWT into a set of distinct frequency band signals, then extract features using a non-linear measure such as band power, energy, or entropy. Various machine learning approaches then classify the generated features. We investigated these methods on EEGs recorded using 19 channels from 29 MCI patients and 32 healthy subjects. In the second step, the study explored the possibility of decreasing the number of EEG channels while preserving, or even enhancing, classification accuracy. We employed multi-objective optimization techniques, such as the non-dominated sorting genetic algorithm (NSGA) and particle swarm optimization (PSO), to achieve this. The results show that the generated DWT-based features resulted in high full-channel classification accuracy scores. Furthermore, selecting fewer channels carefully leads to better accuracy scores. For instance, with a DWT-based approach, the full-channel accuracy achieved was 99.84%. With only four channels selected by NSGA-II, NSGA-III, or PSO, the accuracy increased to 99.97%. Furthermore, NSGA-II selects five channels, achieving an accuracy of 100%. The results show that the suggested DWT-based approaches are promising to detect MCI, and picking the most useful EEG channels makes the accuracy even higher. The use of a small number of electrodes paves the way for EEG-based diagnosis in clinical practice.

Association between Serum Vitamin D Status and Circadian Syndrome: A Cross-Sectional Study.

BACKGROUND: Circadian Syndrome (CircS) encompasses cardiometabolic risk factors and comorbidities, indicating an elevated susceptibility to cardiovascular disease and type 2 diabetes. METHODS: This cross-sectional study aimed to investigate the association between vitamin D levels and each of the following: CircS, metabolic syndrome (MetS), and the individual components of CircS. Data from 14,907 adults who participated in the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018 were utilized. CircS was defined based on MetS components, alongside depression, short sleep, and non-alcoholic fatty liver disease (NAFLD). RESULTS: Our results indicated that low vitamin D levels exhibited meaningful associations with CircS, with vitamin D deficiency and inadequacy demonstrating 2.21-fold (95% CI 1.78-2.74, p < 0.001) and 1.33-fold (95% CI 1.14-1.54, p < 0.001) increases in CircS odds, respectively. The association between vitamin D deficiency and CircS was stronger than that with MetS. Additionally, a dose-response gradient in odds of CircS components, particularly with short sleep duration, was noted as serum vitamin D levels decreased. CONCLUSIONS: our findings highlight a significant association between low serum vitamin D levels and CircS and its components, particularly with short sleep. This suggests a potentially pivotal role of vitamin D in the pathogenesis of Circadian syndrome.

Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.

OBJECTIVES: To develop a machine learning model to accurately predict stroke risk based on demographic and clinical data. It also sought to identify the most significant stroke risk factors and determine the optimal machine learning algorithm for stroke prediction. METHODS: This cross-sectional study analyzed data on 438,693 adults from the 2021 Behavioral Risk Factor Surveillance System. Features encompassed demographics and clinical factors. Descriptive analysis profiled the dataset. Logistic regression quantified risk relationships. Adjusted mutual information evaluated feature importance. Multiple machine learning models were built and evaluated on metrics like accuracy, AUC ROC, and F1 score. RESULTS: Key factors significantly associated with higher stroke odds included older age, diabetes, hypertension, high cholesterol, and history of myocardial infarction or angina. Random forest model achieved the best performance with accuracy of 72.46%, AUC ROC of 0.72, and F1 score of 0.74. Cross-validation confirmed its reliability. Top features were hypertension, myocardial infarction history, angina, age, diabetes status, and cholesterol. CONCLUSION: The random forest model robustly predicted stroke risk using demographic and clinical variables. Feature importance highlighted priorities like hypertension and diabetes for clinical monitoring and intervention. This could help enable data-driven stroke prevention strategies.

The Depression Anxiety Stress Scale 8: investigating its cutoff scores in relevance to loneliness and burnout among dementia family caregivers.

The global trend of advanced aging comes at the cost of amplified onset of age-related diseases. Dementia is a common multifactorial age-related neurodegenerative disorder, which manifests with progressive declines in cognitive functioning and ability to perform activities of daily living. As polices discourage institutionalized care, family members act as primary caregivers and endure increased vulnerability to physical and mental health problems secondary to care-related changes in life routine and relationships. Targeting clinically significant distress at earlier stages through valid brief measures may promote caregivers' wellbeing and dementia care continuity/quality. This study aimed to determine the optimal cutoff score of the Depression Anxiety Stress Scale 8-items (DASS-8) in a convenience sample of 571 European caregivers (Mean age = 53 ± 12 years, Italian = 74.4%, Swiss = 25.6%) through three methods. K-means clustering classified the sample into high- and low-distress clusters based on DASS-8 score of 19. Receiver operator curve (ROC) analysis using 48 and 7 cutoffs of the Zarit Burden Interview (ZBI) and the Three-Item University of California, Los Angeles, Loneliness Scale-version 3 (UCLALS3), revealed two DASS-8 cutoffs (12.5 and 14.5, area under the curve (AUC) = 0.85 and 0.92, p values < .001, 95% CI 0.82-0.88 and 0.89 to 0.94, sensitivity = 0.81 and 0.78, specificity = 0.76 and 0.89, Youden index = 0.57 and 0.67, respectively). Decision modeling produced two DASS-8 cutoffs (9.5 and 14.5) for predicting low and high caregiving burden and loneliness, respectively. According to the median of all DASS-8 cutoffs (14.5) the prevalence of mental distress was 50.8%. Distress correlated with key mental problems such as burnout and loneliness-in path analysis, DASS-8 scores were predicted by the ZBI, UCLALS3, care dependency, and receiving help with care, especially among older, female, and spouse caregivers. Further diagnostic workup should follow to confirm psycho-pathogenicity among caregivers with DASS-8 scores above 14.5. Investigations of the DASS-8 in other countries/populations may confirm the validity of this cutoff score.

Vagal Nerve Stimulation in the Pediatric Population and Correlation Between Family and Treatment Team Perspectives: Single-center Experience.

BACKGROUND: Vagal nerve stimulation (VNS) is an adjunctive therapy to pharmacological treatment in patients with drug-resistant epilepsy. This study aimed to assess the efficacy of VNS therapy for seizure frequency reduction and improving quality of life (QOL) measures in children with refractory epilepsy and to evaluate the correlation between the perspectives of families and those of the treating team. METHODS: This was a prospective cohort study conducted at Abha Maternity and Children's Hospital, Saudi Arabia, from 2018 to 2022. A total of 21 pediatric patients who completed one year of follow-up after VNS implantation were included. Patients were aged between 2 and 14 years, with a mean age of 8.14 ± 3.92; 11 (52.4%) patients were female. Family and physician assessments were collected blinded to each other using Clinical Global Impression of Improvement (CGI-I) scores and QOL assessments to evaluate the correlation between the families' and treating team's perspectives on VNS outcomes. RESULTS: In this study involving 21 patients with intractable epilepsy, VNS showed significant efficacy in reducing the frequency of seizures. VNS significantly reduced the number of seizures per week from a baseline median of 35 to a median of 0.25 at the end of the follow-up period, representing a dramatic reduction of 99.3% (p < 0.001). The number of emergency department visits per year decreased from a baseline median of 12 to a median of 2, a reduction of 83.3% (p < 0.001), whereas the number of hospital admissions per year decreased from a baseline median of 3 to a median of 1, a 66.7% decrease (p < 0.001). The number of antiepileptic medications taken decreased from a median of 4 to 3 (p < 0.001). Notably, 28.57% of the patients achieved complete seizure freedom, and 38% exhibited significant improvement, with at least 50% reduction in seizure frequency. Importantly, none of the patients experienced an escalation in seizure frequency following VNS treatment. The family and physician assessments showed varying degrees of alignment in perceptions, with "concentration" exhibiting a significant positive correlation (r = 0.498, p = 0.022), indicating noteworthy agreement, whereas verbal communication did not show a substantial correlation (r = -0.062, p = 0.791), indicating a divergence of views. CONCLUSION: VNS is a promising and well-tolerated therapy for individuals with intractable seizures, offering clinical benefits and potential enhancements in various aspects of QOL. The varying perceptions between family and physician assessments highlight the importance of considering multiple perspectives when evaluating treatment outcomes.

Whole-Genome Sequence Analysis of Antibiotic Resistance, Virulence, and Plasmid Dynamics in Multidrug-Resistant E. coli Isolates from Imported Shrimp.

We analyzed antimicrobial resistance and virulence traits in multidrug-resistant (MDR) E. coli isolates obtained from imported shrimp using whole-genome sequences (WGSs). Antibiotic resistance profiles were determined phenotypically. WGSs identified key characteristics, including their multilocus sequence type (MLST), serotype, virulence factors, antibiotic resistance genes, and mobile elements. Most of the isolates exhibited resistance to gentamicin, streptomycin, ampicillin, chloramphenicol, nalidixic acid, ciprofloxacin, tetracycline, and trimethoprim/sulfamethoxazole. Multilocus sequence type (MLST), serotype, average nucleotide identity (ANI), and pangenome analysis showed high genomic similarity among isolates, except for EC15 and ECV01. The EC119 plasmid contained a variety of efflux pump genes, including those encoding the acid resistance transcriptional activators (gadE, gadW, and gadX), resistance-nodulation-division-type efflux pumps (mdtE and mdtF), and a metabolite, H1 symporter (MHS) family major facilitator superfamily transporter (MNZ41_23075). Virulence genes displayed diversity, particularly EC15, whose plasmids carried genes for adherence (faeA and faeC-I), invasion (ipaH and virB), and capsule (caf1A and caf1M). This comprehensive analysis illuminates antimicrobial resistance, virulence, and plasmid dynamics in E. coli from imported shrimp and has profound implications for public health, emphasizing the need for continued surveillance and research into the evolution of these important bacterial pathogens.