Early Intervention Measures Research Articles

Prevalence and risk factors of diastasis recti abdominis in the long-term postpartum: a cross-sectional study

The aim of this study was to investigate the prevalence of diastasis recti abdominis (DRA) and to find the possible risk factors for DRA based on two diagnostic criteria among women in long-term postpartum period. A cross-sectional study was conducted on 1000 postpartum women at five different time points (3, 5, 10, 20, and 30 years after partum, respectively). Inter-recti distance (IRD) was assessed using ultrasound imaging, while basic data were collected through self-reported questionnaires. Two diagnostic criteria, IRD > 2 cm and IRD > 3 cm, were used to present the prevalence of DRA. Univariate and multivariate analyses were employed to explore the risk factors for DRA. When using the diagnostic criterion of IRD > 2 cm for DRA, the prevalence was 36%, 31%, 22%, 26%, and 30% at 3, 5, 10, 20, and 30 years postpartum, respectively. When using the diagnostic criterion of IRD > 3 cm for DRA, the prevalence was 13%, 8%, 6%, 8%, and 10%, respectively. The results revealed that higher body mass index (BMI) was a risk factor for DRA at 10 years postpartum (p = 0.000), bigger number of parturitions was a risk factor at 3, 5, and 10 years postpartum (p = 0.000, 0.004 and 0.000), twins was a risk factor at 3 years postpartum (p = 0.001), and diabetes was a risk factor at 20 and 30 years postpartum (P = 0.000 and 0.004). (1) The prevalence of DRA in the long-term postpartum period was high, especially when IRD > 2 cm was used as the diagnostic criterion. (2) Higher BMI, bigger number of parturitions, twins and diabetes were independently associated with higher odds of DRA at different long-term postpartum time points. (3) The diagnostic criterion for DRA should be further improved to avoid excessive clinical diagnosis and treatment. (1) This study elucidated the prevalence and risk factors associated with long-term postpartum DRA, thereby enhancing clinical awareness and advocating for early intervention and prevention measures to mitigate serious adverse outcomes. (2) This study presented the research results using two diagnostic criteria, increasing comparability with existing studies. In addition, discussions on reasonable diagnostic criterion for DRA were also conducted to avoid excessive clinical diagnosis and treatment.

Beyond Decay: Exploring the Age-associated Variations in Streptococcus mutans and Lactobacillus in Dental Caries.

Streptococcus mutans and Lactobacilli play an important role in the etiopathogenesis and progression of dental caries (DC). Their quantification and identification may be helpful for epidemiological and early intervention measures. We conducted the study to evaluate the colony counts of S. mutans and Lactobacillus with the location of DC and correlate their prevalence with the age of the patient. The study population comprised 60 patients with DC. They were divided into two groups according to age, and each group was further divided into three subgroups based on involvement of enamel, dentin, and pulp by DC. The swab samples were collected, and organisms were isolated using Mitis Salivarius Bacitracin (MSB) Agar and Lactobacillus MRS Agar. Manual counting of colonies on plates illuminated by transmitted light was done. Results were summarized and analyzed statistically. The caries prevalence was found to be higher in children, with females being more affected. In both groups, posterior teeth were more affected, and occlusal/incisal surface caries were more common. The mean colony count of S. mutans (61.3%) and Lactobacillus (63.4%) was significantly higher in group I compared to group II. In both groups, the mean colony counts of S. mutans were higher in enamel, followed by dentin and pulp. In contrast, in both groups, the mean colony counts of Lactobacillus were higher in pulp, followed by dentin and enamel. Bacterial colony counts may help in taking specific measures against specific organisms and thereby prevent the development of new carious lesions. Tandon A, Srivastava A, Singh P, et al. Beyond Decay: Exploring the Age-associated Variations in Streptococcus mutans and Lactobacillus in Dental Caries. Int J Clin Pediatr Dent 2024;17(9):993-998.

Relationship of proteins and subclinical cardiovascular traits in the population-based LIFE-Adult study

Background and aimsUnderstanding molecular processes of the early phase of atherosclerotic cardiovascular disease conditions is of utmost importance for early prediction and intervention measures. MethodsWe measured 92 cardiovascular-disease-related proteins (Olink, Cardiovascular III) in 2024 elderly participants of the population-based LIFE-Adult study. We analysed the impact of 27 covariables on these proteins including blood counts, cardiovascular risk factors and life-style-related parameters. We also analysed protein associations with 13 subclinical cardiovascular traits comprising carotid intima media thickness, plaque burden, three modes of Vicorder-based pulse-wave velocities, ankle-brachial index and ECLIA-based N-terminal prohormone of brain natriuretic peptide (NT-proBNP). ResultsEstimated glomerular filtration rate, triglycerides and sex where the most relevant covariables explaining more than 1 % variance of 49, 22 and 20 proteins, respectively. A total of 43 proteins were significantly associated with at least one of the analysed subclinical cardiovascular traits. NT-pro-BNP, brachial-ankle pulse-wave velocity (baPWV) and parameters of carotid plaque burden accounted for the largest number of associations. Association overlaps were relatively sparse. Only growth/differentiation factor 15, low density lipoprotein receptor and interleukin-1 receptor type 2 are associated with these three different cardiovascular traits. We confirmed several literature findings and found yet unreported associations for carotid plaque presence (von-Willebrand factor, galectin 4), carotid intima-media thickness (carboxypeptidase A1 andB1), baPWV (cathepsin D) and NT-proBNP (cathepsin Z, low density lipoprotein receptor, neurogenic locus homolog protein 3, trem-like transcript 2). Sex-interaction effects were observed, e.g. for spondin-1 and growth/differentiation factor 15 likely regulated by androgen response elements. ConclusionsWe extend the catalogue of proteome biomarkers possibly involved in early stages of cardiovascular disease pathologies providing targets for early risk prediction or intervention strategies.

Analysis of Influencing Factors of Moderate to Severe Malnutrition in Elderly Patients with Lung Cancer during Chemotherapy

Objective: This study aims to identify the factors influencing moderate to severe malnutrition in elderly lung cancer patients undergoing chemotherapy and to provide a basis for developing clinical prevention and intervention strategies. Methods: The clinical data of 150 elderly lung cancer patients who received chemotherapy were retrospectively analyzed. Demographic data, clinical characteristics, laboratory indicators, treatment plans, and other relevant information were collected. Independent risk factors for moderate to severe malnutrition during chemotherapy were identified. Results: During chemotherapy, 50 patients (33.33%) developed moderate to severe malnutrition. The BMI of patients with moderate to severe malnutrition was significantly lower than that of patients with no or mild malnutrition (21.20 ± 1.60 vs. 26.14 ± 2.31, P < 0.001), and the proportion of patients with stage IV tumors was significantly higher (60.00% vs. 27.00%, P < 0.001). Serum pre-albumin (pre-ALB) and hemoglobin (Hb) levels in patients with moderate to severe malnutrition were significantly lower than those in patients with no or mild malnutrition (152.67 ± 30.41 g/L vs. 252.47 ± 51.24 g/L and 102.44 ± 10.09 g/L vs. 154.21 ± 15.18 g/L, respectively, P < 0.001 for all). Conclusion: Low BMI, decreased serum pre-ALB levels, and decreased serum Hb levels before chemotherapy are independent risk factors for moderate to severe malnutrition in elderly lung cancer patients during chemotherapy. Close clinical attention should be given to these patients, with early intervention measures such as nutritional support to reduce the incidence of malnutrition and improve patients’ quality of life and prognosis.

The Cardiac Manifestations of Post-streptococcal Infection in Paediatrics: Case Series

Introduction: Group A streptococcus (GAS) remains a global burden, particularly in developing countries, with long-term morbidity and mortality complications. Different strains and immune responses to GAS can lead to various disease patterns, from predominantly throat and skin manifestations to renal and cardiac involvement. Myocarditis associated with glomerulonephritis can progress to carditis, which may present with signs and symptoms similar to rheumatic heart disease. Although uncommon, this condition can deteriorate into dilated cardiomyopathy, potentially leading to cardiogenic shock and death, necessitating early recognition, intervention, and preventive measures. Case Presentation: We report three pediatric cases of post-streptococcal infection with cardiac manifestations. The first case involves an 8-year-old female with palpitations and a recurring cough, diagnosed with acute rheumatic carditis. The second case is a 10-year-old female presenting with hemoptysis and edema in the lungs and extremities due to acute rheumatic carditis and prior suspected vegetation. The third case is a 9-year-old male initially diagnosed with post-streptococcal glomerulonephritis (PSGN), who deteriorated into dilated cardiomyopathy with an ejection fraction of 32% and multiple organ dysfunction syndrome. All three patients recovered well, received prophylactic management, and were referred for further investigation. Conclusions: Identifying specific features on echocardiography can help differentiate between rheumatic heart disease (RHD) and PSGN-associated cardiac involvement. Additionally, echocardiography is a more readily available and cost-effective screening method, especially in endemic populations. Early recognition and management of post-streptococcal cardiac complications may prevent long-term morbidity and mortality.

Onset and progression of dental erosion in a mouse model.

Purpose of this research was to examine the onset, progression and wear rates of dental erosion in an established mouse model. Dental erosion in mice was experimentally induced, and the acidic effects of cola drink on their teeth after 2, 4 and 6-weeks were closely analysed by scanning electron microscopy. The tooth height and enamel or dentin loss were established. Results: The dental erosion on the molars showed clear progression from 2 to 6 weeks. By the 2-week mark, a significant portion of enamel was already eroded, revealing the dentin on the lingual cusps. When adjusted for attritional wear, molars exposed to cola for 2 weeks showed a 35% drop in lingual tooth height compared to controls (533 μm vs. 818 μm). At 4 and 6 weeks, the cola-exposed group continued to display decreased lingual tooth heights by 40% (476 μm vs. 799 μm) and 43% (440 μm vs. 767 μm), respectively. This study revealed significant acidic effects of cola drink on mouse molars as early as 2 weeks. These findings highlight the challenge of monitoring dental erosion clinically and underscore the importance of early preventive and intervention measures.

Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients.

Current Status of Outcomes Reported by Patients With Stroke and an Analysis of Influencing Factors: Cross-Sectional Questionnaire Study.

Stroke is the leading cause of acquired disability and the second leading cause of death worldwide. Its rate of incidence, disability, mortality, and recurrence is high, and the patients experience various symptoms of discomfort, which not only affect their rehabilitation function but also reduce their ability to perform daily activities and their quality of life. Nowadays, with the improvement of China's medical standards, patients are increasingly attentive to their quality of life and health status. However, diagnostic techniques and effective treatments for patients with stroke are still limited but urgently required. This study aimed to evaluate the quality of life during hospitalization using a stroke patient-reported outcomes (PROs) scale and additionally to recognize potential factors and risk indicators that may impact recurrent events, facilitating early intervention measures. This is a registry-based, retrospective observational cross-sectional study on patients with stroke. A convenient sampling method was used to select various indicators of patients. The Stroke-PRO scale was then used to assess patients' conditions across physical, psychological, social, and therapeutic domains. Multiple linear regression analysis was applied to identify factors influencing stroke PROs, while correlation analysis was conducted to explore the relationship between these outcomes and blood lipid levels. The mean Stroke-PRO score in this study was 4.09 (SD 0.29) points. By multiple linear regression analysis, residence, occupation, physical exercise, Barthel index, Braden scale, National Institutes of Health Stroke Scale scores at admission, and stroke type were the risk factors for reported outcomes of patients with stroke (P<.05). Correlation analysis showed that serum triglyceride, total cholesterol, and low-density lipoprotein were negatively correlated with Stroke-PRO scores in patients with stroke (P<.05), while high-density lipoprotein was positively correlated with patients with stroke (P<.05). The 95% CI was -0.31 to -0.03 for triglyceride, 0.17-0.44 for high-density lipoprotein, -0.29 to -0.01 for cholesterol, -0.30 to -0.02 for low-density lipoprotein, and -0.12 to 0.16 for blood glucose. Patients with stroke have a low level of health, and their reported outcomes need to be improved. Accordingly, nursing staff should pay attention to the quality of life and blood lipid indexes of patients with stroke, actively assess their actual health status, and take early intervention measures to promote their recovery.

Understanding Major Depressive Disorder in Singapore: Insights from the second Singapore Mental Health Study (SMHS 2016)

BackgroundMajor Depressive Disorder (MDD) poses significant public health challenges globally and in Southeast Asia, with substantial impairment and disease burden. Understanding its prevalence and associated risk factors is crucial for effective intervention. MethodsThis study aims to describe the prevalence shifts, correlates, and comorbidities of MDD in Singapore. Data were collected from the second Singapore Mental Health Study (SMHS 2016), a national cross-sectional survey comprising 6126 adult residents. The WHO Composite International Diagnostic Interview assessed MDD and comorbidities. Statistical analyses, including logistic regression, were conducted to examine the associations and trends. ResultsThe lifetime weighted prevalence of MDD in SMHS 2016 was 6.2 %, with an overall increase from 5.8 % in 2010. Significant associations were found between MDD and age, marital status, and comorbid physical disorders. Young adults and divorced/separated individuals exhibited higher MDD prevalence. Chronic pain was significantly associated with MDD. LimitationsThe study's cross-sectional design limits causal inference, and selective non-response might affect prevalence estimates. However, the study benefits from a large, nationally representative sample and standardized methodologies. ConclusionsYoung and middle-aged adults, and divorced/separated individuals demonstrate elevated MDD prevalence, warranting targeted interventions. Individuals with comorbidities, particularly chronic pain, constitute a high-risk and vulnerable population. Comprehensive assessment and treatment plans should involve multidisciplinary teams and integrated care approaches to better address the complex needs of these individuals. Our study also highlights specific interventions for schools, families, communities, and workplaces. Despite Singapore's relatively low prevalence compared to Western nations, MDD remains cross-culturally valid emphasizing the need for early intervention and preventive public health measures.

Development of Metal-Oxide-Metal Micro-Well Array Biosensors for Highly Sensitive Detection of Covid-19

Biosensors play a crucial role in various fields, including medical diagnostics, environmental monitoring, and food safety. Enhancing the sensitivity of biosensors is essential for improving their performance and reliability. One effective approach to achieve this goal is by reducing the distance of electrodes, a parameter that significantly influences the sensor's response. Employing vertical structures to reduce electrode gaps offers several advantages. the heightened controllability of vertical structures facilitates easy adjustments to element size and shape, catering to specific application requirements and maximizing sensor performance. The choice of vertical structures presents a more accessible, flexible, and cost-effective solution from a manufacturing standpoint, particularly for large-scale sensor production.On the other hands, DNA testing plays a pivotal role in the context of COVID-19, demonstrating its profound significance in the identification, monitoring, and management of the virus. Through the analysis of genetic material, particularly RNA in the case of COVID-19, DNA testing enables accurate and timely diagnosis of infections. This precision is instrumental in differentiating the virus from other respiratory illnesses, facilitating early intervention and containment measures. Moreover, the genetic insights garnered from DNA testing contribute to a better understanding of the virus's behavior, mutations, and potential therapeutic targets, thereby informing public health strategies and vaccine development. The integration of DNA testing in the comprehensive approach to combating COVID-19 underscores its indispensable role in mitigating the impact of the pandemic on global health.In this study, we present the development of a novel biosensor based microwell array structure. The fabrication process employed a layer-by-layer method. To be brief, a 4-inch silicon wafer with a 300 nm thick thermal SiO2 was cleaned in acetone, isopropanol, and water. Subsequently, the bottom electrode was deposited by E-gun evaporator followed by a lift-off process. An extremely thin 45nm aluminum oxide layer was deposited by Atomic Layer Deposition (ALD). Then, a 100nm gold layer was deposited as the top electrode by E-gun. Finally, conclude the fabrication by defining the sensing area of the microwell array through Lithography and several etching steps.For the DNA immobilization process, the Covid-probe ssDNA was incubated with the biosensor at 4℃ within the PDMS wells overnight, followed by rinsed 3 times. And 1 uM Covid-target ssDNA was incubated after immobilization of probe DNA. The condition for the reaction of target and probe was 1 hour at room temperature, followed by rinsed 3 times in DI water.The detection was achieved using a combination of cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), collectively offering a comprehensive approach for accurate and reliable measurements. The electrolyte consists of 1 mM KCl solution as supporting electrolytes and 5 mM ferricyanide/ferrocyanide as redox pair. The Rct charge transfer resistance of bare device is around 1320 Ohm due to the small sensing area of around 500 * 500 um2. After incubation with the probe DNA, the Rct is around 2150 Ohm indicating that the probe DNA binds to the Au surface successfully. Target DNA was quantitatively measured over a wide concentration range, spanning from 10 pM to 10 nM. The Rct variance from 2260 to 4970 Ohm.Our results demonstrate the biosensor's exceptional performance in terms of sensitivity and dynamic range, showcasing its potential application in the early and accurate diagnosis of Covid-19. The micro-well array biosensor, with its unique design and integrated electrochemical measurement strategy, holds promise for future advancements in point-of-care diagnostics and environmental monitoring. Figure 1

Central nervous injury risk factors after endovascular repair of a thoracic aortic aneurysm with type B aortic dissection.

Aortic dissection is the deadliest disease of the cardiovascular system. Type B aortic dissection accounts for 30%-60% of aortic dissections and is mainly treated by endovascular repair of thoracic endovascular aneurysm repair (TEVAR). However, patients are prone to various complications after surgery, with central nervous system injury being the most common, which seriously affects their prognosis and increases the risk of disability and death. Therefore, exploring the risk factors of central nervous system injury after TEVAR can provide a basis for its prevention and control. To investigate the risk factors for central nervous system injury after the repair of a thoracic endovascular aneurysm with type B aortic dissection. We enrolled 306 patients with type B aortic dissection who underwent TEVAR at our hospital between December 2019 and October 2022. The patients were categorized into injury (n = 159) and non-injury (n = 147) groups based on central nervous system injury following surgery. The risk factors for central nervous system injury after TEVAR for type B aortic dissection were screened by comparing the two groups. Multivariate logistic regression analysis was performed. The Association between age, history of hypertension, blood pH value, surgery, mechanical ventilation, intensive care unit stay, postoperative recovery times on the first day after surgery, and arterial partial pressure of oxygen on the first day after surgery differed substantially (P < 0.05). Multivariate logistic regression analysis indicated that age, surgery time, history of hypertension, duration of mechanical ventilation, and intensive care unit stay were independent risk factors for central nervous system injury after TEVAR of type B aortic dissection (P < 0.05). For high-risk patients with central nervous system injury after TEVAR of type B aortic dissection, early intervention measures should be implemented to lower the risk of neurological discomfort following surgery in high-risk patients with central nervous system injury after TEVAR for type B aortic dissection.

Exploring the predictive factors of heart disease using rare association rule mining

Cardiovascular diseases continue to be the leading cause of mortality worldwide, claiming a significant number of lives each year. Despite the advancements in predictive models, including logistic regression, neural networks, and random forests, these techniques often lack transparency and interpretability, limiting their practical application in clinical settings. To address this challenge, this research introduces EPFHD-RARMING, an innovative approach designed to enhance the understanding and predictability of heart disease through the discovery of rare and meaningful patterns. EPFHD-RARMING utilizes rare association rule mining to uncover hidden and unexpected rules that identify critical factors contributing to heart disease. This method is particularly adept at identifying high-risk patterns in individuals who appear healthy but may develop heart disease under certain conditions, thus facilitating early intervention and preventive measures. By integrating these insights with established feature engineering techniques, EPFHD-RARMING enhances its practical utility, enabling medical professionals to proactively manage patient care and tailor interventions to individual risk profiles. This study demonstrates the effectiveness of EPFHD-RARMING in providing a deeper, actionable understanding of the complex dynamics of heart disease. The model’s ability to identify and interpret rare patterns holds significant promise for advancing medical analytics and improving patient outcomes. Moreover, the applicability of EPFHD-RARMING extends beyond the healthcare domain, offering valuable insights in various fields where the discovery of rare patterns is critical, such as finance, marketing, and cybersecurity. This study conducts a comprehensive evaluation, which demonstrates the superior performance of EPFHD-RARMING compared to traditional predictive models in identifying key factors contributing to heart disease, in terms of interestingness, explainability, and comprehensiveness of insights. The results underscore the potential of this innovative approach to revolutionize our understanding and prediction of heart disease, ultimately contributing to more effective and personalized healthcare solutions. This research emphasizes the importance of rare association rule mining in medical analytics and paves the way for future studies to explore and utilize these techniques across diverse domains.

A novel prediction model for the probability of aggressive behavior in patients with mood disorders: Based on a cohort study

BackgroundAccurately predicting the probability of aggressive behavior is crucial for guiding early intervention in patients with mood disorders. MethodsCox stepwise regression was conducted to identify potential influencing factors. Nomogram prediction models were constructed to predict the probabilities of aggressive behavior in patients with mood disorders, and their performance was assessed using consistency index (C-index) and calibration plots. ResultsResearch findings on 321 patients with mood disorders indicated that being older (HR = 0.92, 95% CI: 0.86–0.98), single (HR = 0.11, 95% CI: 0.02–0.68), having children (one child, HR = 0.07, 95%CI: 0.01–0.87; more than one child, HR = 0.33, 95%CI: 0.04–2.48), living in dormitory (HR = 0.25, 95%CI: 0.08–0.77), non-student (employee, HR = 0.24, 95% CI: 0.07–0.88; non-employee, HR = 0.09, 95% CI: 0.02–0.35), and higher scores in subjective support (HR = 0.90, 95% CI: 0.82–0.99) were protective factors. On the contrary, minorities (HR = 5.26, 95% CI: 1.23–22.48), living alone (HR = 4.37, 95% CI: 1.60–11.94), having suicide history (HR = 2.51, 95% CI: 1.06–5.95), and having higher scores in EPQ-E (HR = 1.04, 95% CI: 1.00–1.08) and EPQ-P (HR = 1.03, 95% CI: 1.00–1.07) were identified as independent risk factors for aggressive behavior in patients with mood disorders. The nomogram prediction model demonstrated high discrimination and goodness-of-fit. ConclusionsA novel nomogram prediction model for the probability of aggressive behavior in patients with mood disorders was developed, effective in identifying at-risk populations and offering valuable insights for early intervention and proactive measures.

The impact of vitamin D status on lipid profiles and atherogenic dyslipidemia markers in children and adolescents with obesity

Background and aimAdequate serum vitamin D levels correlate with a more favorable lipid profile compared to deficient levels. Despite the well-established prevalence of vitamin D deficiency in children with obesity, studies investigating its influence on lipid profiles in this population are scarce. We explored the impact of vitamin D status on lipid profiles and markers of atherogenic dyslipidemia in a cohort of children and adolescents with obesity. Methods and resultsA total of 271 Caucasian children and adolescents with overweight/obesity and a control group of 54 pediatric patients with normal weight. All participants underwent outpatient visits for the assessment of clinical parameters and venous blood collection for biochemical analysis such as triglycerides (TG)/HDL-C ratio, LDL-C/HDL-C ratio, atherogenic index of plasma AIP), vitamin D level.Individuals with obesity displayed severe vitamin D deficiency (25-OH-D ≤10 ng/ml) at a higher frequency compared to those with normal weight (p = 0.03). In patients with overweight/obesity and low 25-OH-D levels show higher values of glycemia (p = 0.001), insulin resistance (HOMA-IR and TRYG p < 0.001), TG (p < 0.001), TG/HDL-C (p = 0.001), AIP (p < 0.001), SBP (p = 0.01), and DBP (p = 0.04). In normal-weight individuals with low 25-OH- D levels an increased values of glycemia (p = 0.01), insulin resistance (HOMA-IR p = 0.01 and TRYG p = 0.002), TG (p = 0.01), TG/HDL-C (p = 0.02), AIP (p = 0.01). A direct correlation between 25-OH-D levels and metabolic parameters is observed. ConclusionsA correlation between vitamin D levels and the lipid/atherosclerotic profile was recorded. Vitamin D deficiency may represent a preventable and easily treatable cardiometabolic risk factor, emphasizing the importance of early intervention and preventive measures.

The Role of Screening, Risk Factors, and Early Intervention in Preventing Diabetes in the Obese Population: A Systematic Review.

With its rising global prevalence, diabetes has become one of the most significant and challenging health problems afflicting the world's population today. The increasing burden of diabetes and its associated complications calls for immediate action for prevention which primarily includes addressing the risk factors.The most significant risk factor for the onset of diabetes is obesity. Obesity and diabetes rates have been rising simultaneously, posing a threat to patient mortality and driving up community healthcare costs. A weight loss of five percent or more of total body weight has been shown to improve the quality of life, reduce the need for pharmacological therapy for diabetes, and enhance glycemic control. This level of weight loss can have significant health benefits, particularly for individuals with diabetes or at risk for developing diabetes. We aim to conduct this systematic review to assess diverse risk factors contributing to the incidence of diabetes among the obese population and determine various preventive strategies and recommendations in practice for the prevention of diabetes in this cohort. As a result, we included original studies that recruited the obese and diabetic populations and defined preventive measures for early intervention. Additionally, we included studies published in the last 10 years (2014-2024) only for the latest evidence. Studies including obese populations with cardiovascular diseases and neurological disorders were excluded. The Newcastle-Ottoman Castle assessment tool was utilized to assess the quality of the studies. We included nine studies that recruited 60,645 patients and were published between 2015 and 2022. Findings suggest that obesity alone is a significant contributor to the occurrence or onset of diabetes. At the same time, the presence of other risk factors, including hypertension, dyslipidemia, elevated triglycerides, or HDL and LDL levels, may further increase the risk of diabetes and its associated complications among the obese population. Preventive strategies emphasize early intervention through increasing awareness and educating communities about risk factors and lifestyle interventions, including the limitations of fast food diets for the prevention of diabetes and weight control. Since obesity is considered to be an independent risk factor for the development of diabetes, addressing and managing it is of critical importance clinically. Targeted early interventions, including screening for risk factors, health promotion, and education activities, can aid in the adaptation of healthier lifestyles, which can reduce the burden of these diseases significantly.

Automatic dysarthria detection and severity level assessment using CWT-layered CNN model

Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models’ performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal’s integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process.

The Factors for the Occurrence of Pulmonary Infection after Gastrointestinal Surgery and the Construction of a Predictive Model Using sTREM-1 and TIM-4: A Retrospective Study.

Identifying and intervening with high-risk postoperative pulmonary infections patients pose challenges in clinical practice. This study aims to conduct a comprehensively analysis of the risk factors and predictive factors associated with post-gastrointestinal surgery pulmonary infections and to develop a predictive model that can predict occurrence of pulmonary infection. A retrospective analysis was conducted on 96 patients who underwent gastrointestinal surgery at our hospital from May 2021 to October 2023. The occurrence rate of postoperative pulmonary infections was calculated, and patients were categorized into two groups: those with pulmonary infections (the occurrence group) and those without pulmonary infections (the non-occurrence group). Logistic regression analysis was utilized to identify the risk factors for post-gastrointestinal surgery pulmonary infections and to evaluate the predictive value of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) and T cell immunoglobulin and mucin domain-4 (TIM-4) using nomograms, calibration curves, and Receiver Operating Characteristic (ROC) curves. Out of 96 patients, 20 (20.83%) developed postoperative pulmonary infections. Significant differences were noted between occurrence and non-occurrence groups in terms of smoking (65.00% vs. 34.21%, p = 0.013), surgical duration (70.00% vs. 31.58%, p = 0.002), Preoperative hemoglobin level (35.00% vs. 65.79%, p = 0.013), sTREM-1 levels (23.57 ± 3.16 pg/mL vs. 15.62 ± 2.48 pg/mL, p < 0.001), and TIM-4 levels (61.48 ± 6.35 pg/mL vs. 44.73 ± 5.22 pg/mL, p < 0.001). Logistic regression analysis leads to the development of a risk prediction model for post-gastrointestinal surgery pulmonary infections. The high predictive values of sTREM-1 (Area Under Curve (AUC) = 0.962, 95% confidence interval (CI) 0.917~0.999) and TIM-4 (AUC = 0.970, 95% CI 0.925~1.000) were highlighted by the AUC values, underscoring their clinical importance. A predictive model utilizing sTREM-1 and TIM-4 for pulmonary infection following gastrointestinal surgery was developed. Additionally, other risk factors such as smoking, surgical duration, and preoperative hemoglobin level were evaluated. This finding can be applied in clinical practice to identify potentially susceptible patients and facilitate early intervention measures.

Optimising a clinical decision support tool to improve chronic kidney disease management in general practice

BackgroundEarly identification and treatment of chronic disease is associated with better clinical outcomes, lower costs, and reduced hospitalisation. Primary care is ideally placed to identify patients at risk of, or in the early stages of, chronic disease and to implement prevention and early intervention measures. This paper evaluates the implementation of a technological intervention called Future Health Today that integrates with general practice EMRs to (1) identify patients at-risk of, or with undiagnosed or untreated, chronic kidney disease (CKD), and (2) provide guideline concordant recommendations for patient care. The evaluation aimed to identify the barriers and facilitators to successful implementation.MethodsFuture Health Today was implemented in 12 general practices in Victoria, Australia. Fifty-two interviews with 30 practice staff were undertaken between July 2020 and April 2021. Practice characteristics were collected directly from practices via survey. Data were analysed using inductive and deductive qualitative analysis strategies, using Clinical Performance - Feedback Intervention Theory (CP-FIT) for theoretical guidance.ResultsFuture Health Today was acceptable, user friendly and useful to general practice staff, and supported clinical performance improvement in the identification and management of chronic kidney disease. CP-FIT variables supporting use of FHT included the simplicity of design and delivery of actionable feedback via FHT, good fit within existing workflow, strong engagement with practices and positive attitudes toward FHT. Context variables provided the main barriers to use and were largely situated in the external context of practices (including pressures arising from the COVID-19 pandemic) and technical glitches impacting installation and early use. Participants primarily utilised the point of care prompt rather than the patient management dashboard due to its continued presence, and immediacy and relevance of the recommendations on the prompt, suggesting mechanisms of compatibility, complexity, actionability and credibility influenced use. Most practices continued using FHT after the evaluation phase was complete.ConclusionsThis study demonstrates that FHT is a useful and acceptable software platform that provides direct support to general practice in identifying and managing patients with CKD. Further research is underway to explore the effectiveness of FHT, and to expand the conditions on the platform.

Prevalence and risk factors of parental mental health problems: A cross-sectional study.

An understanding of the prevalence and risk factors of parental mental health problems is important for early intervention and prevention measures and shaping services for parents and their children. However, large representative surveys of parental mental health problems and associated risk factors are lacking. The aim of this study was to estimate prevalence rates of parental mental health problems using a standardised measure of psychiatric morbidity (General Health Questionnaire; GHQ-12), in a representative sample of parents and caregivers of children and young people (2-19 years) in Northern Ireland. Further, this study explored associated risk factors of parental mental health problems. A random household survey of parents and children was conducted between June 2019 and March 2020. Parental responses on demographic, economic, familial and psychological measures were collected (N = 2815) and 22% of parents and caregivers screened positive for mental health problems. The STROBE checklist for observational research was adhered to. Multivariate logistic regression indicated that being in receipt of benefits, having poor family support, a history of adverse childhood experiences, a history of exposure to politically motivated violence (the Troubles), and a child with conduct problems and poor health were all independent risk factors of increased parental mental health problems. Findings will help to inform future commissioning and development of services and broaden understanding of the correlates of parental mental health problems.

Key results from observational studies and real-world evidence of sodium-glucose cotransporter-2 inhibitor effectiveness and safety in reducing cardio-renal risk.

Sodium-glucose cotransporter 2 (SGLT2) inhibitors, originally designed to manage blood sugar levels in individuals with type 2 diabetes (T2D), have emerged as a crucial class of drugs for managing cardio-renal diseases. These drugs work by targeting the SGLT2 protein in the kidneys, promoting the excretion of glucose and influencing metabolic pathways beyond glucose control. The relationship between cardio-renal diseases and SGLT2 inhibitors has been explored through landmark trials and real-world evidence (RWE) studies, demonstrating significant reductions in cardio-renal complications. This review discusses the importance of RWE studies alongside randomized controlled trials in understanding the real-world effectiveness and safety of SGLT2 inhibitors. It outlines the advantages and disadvantages of RWE compared to RCTs, highlighting their complementary roles in providing comprehensive insights into treatment outcomes. By examining a range of RWE studies, the review underscores the cardio-renal benefits of SGLT2 inhibitors across various patient populations. Safety assessments indicate that SGLT2 inhibitors are generally well tolerated, with severe adverse events being rare. Common issues, such as genital mycotic infections and urinary tract infections, are acknowledged, alongside less frequent but significant adverse events including diabetic ketoacidosis, lower-limb amputations, and bone fractures. In summary, SGLT2 inhibitors show promising cardio-renal protective effects in real-world scenarios across diverse populations in T2D, indicating their potential as early intervention measures. Continued research is essential for gaining a thorough understanding of their long-term effects and safety profiles.