Individual Risk Research Articles

Regional investment risk evaluation based on compound risk correlation coefficient and migration learning approach

The high profit of regional investment is often accompanied by high risks, and the prior assessment of investment risks is conducive to avoid investment risks. However, the traditional evaluation methods usually only focus on individual risk factors, and it is difficult to evaluate and manage risks on the whole. Therefore, the study introduces deep learning algorithm, first build regional investment risk evaluation index system, then according to the characteristics of risk evaluation, design based on deep learning regional investment risk evaluation model, the final use parameter based migration learning algorithm and composite correlation coefficient to improve the evaluation model, solve the problem of insufficient training samples. The test results showed that the randomly selected 50 test samples with two different risk assessment models were 0.80 and 0.86, the deep learning algorithm tested 0.84, and the transfer learning improved model tested was 0.92, with the highest accuracy. This shows that the deep learning regional investment risk evaluation model improved by transfer learning effectively solves the problem of insufficient training data and improves the accuracy of prediction evaluation. In the field of venture capital, the model can help investors to evaluate and predict investment risks more accurately and improve the effect of investment decisions.

Impaired perception of cardiovascular risk and health literacy in individuals with high cardiovascular diseases risk from different socioeconomic levels in Antalya, Turkey.

This study aimed to determine how the frequency of impaired perception of cardiovascular disease (CVD) risk is correlated with health literacy in individuals with high CVD risk in two regions with different socioeconomic levels. A descriptive study was conducted. The Systematic Coronary Risk Estimation, recommended by the European and Turkish Cardiology Societies, provides recommendations based on individuals' actual CVD risk levels. According to the Systematic Coronary Risk Estimation, participants suffering from diseases were identified as high and very high risk. The perception of risk was determined by subjectively assessing the risk level of the individuals. The European Health Literacy Survey Questionnaire was used. The Mann-Whitney U test, Chi-squared test and Spearman's correlation analysis were used to analyse the data. We also conducted a logistic regression analysis to identify factors contributing to impaired perception of CVD risk. A total of 384 individuals, aged between 40 and 70years (with a mean age of 58.35±8.33years), were included in the study. These individuals had a high risk of CVD and were registered to family health centres from two different socioeconomic levels. Out of the 384 participants, 201 individuals belonged to the lower socioeconomic level region and 183 individuals belonged to the higher socioeconomic level region. A total of 61.7% of the participants with high CVD risk had an impaired perception of CVD risk. The health literacy level of 59.9% of individuals was either inadequate or problematic. The health literacy score of participants with impaired risk perception (29.59±9.07) was lower than those with correct risk perception (35.83±10.94; P <0.001). Factors that affected the perception of CVD risk included age, education level, working status, occupation, a family history of CVD, the CVD risk assessment by health professionals and the recommendation of health professionals to individuals for CVD screening. It is necessary for health care professionals to make risk assessments and perform interventions to improve health literacy to raise the awareness of individuals with high CVD risk on their actual risk.

Comprehensive Assessment of Environmental Emissions, Fate, and Risks of Veterinary Antibiotics in China: An Environmental Fate Modeling Approach.

China is one of the major global consumers of veterinary antibiotics. Insufficient recognition of emissions and environmental contamination hamper global efforts to prevent antibiotic resistance development. This pioneering study combined empirical data and modeling approaches to predict total 2010-2020 emissions of 80 veterinary antibiotics ranging from 23,110 to 40,850 tonnes/year, after 36-50% antibiotic removal by manure treatment. Following an initial increase of 10% from 2010 to 2015, emissions declined thereafter by 43%. While 85% of emissions discharged into soils, approximately 56%, 23%, and 18% of environmental residue were ultimately distributed in soils, freshwaters, and seawaters under steady-state conditions. In 2020, 657 (319-1470) tonnes entered the ocean from inland freshwaters. Median ∑antibiotics concentrations were estimated at 4.7 × 103 ng/L in freshwaters and 2.9 ng/g in soils, with tetracyclines and sulfonamides as the predominant components. We identified 44 veterinary antibiotics potentially posing high risks of resistance development in freshwaters, with seven exhibiting high risks in >10% of Chinese freshwater areas. Tetracyclines were the category with the most antibiotics exhibiting elevated risks; however, sulfamethylthiazole demonstrated the highest individual compound risk. The Haihe River Basin displayed the highest susceptibility overall. The findings offer valuable support for control of veterinary antibiotic contamination in China.

Prevention of recurrence of urolithiasis

Urolithiasis is one of the most frequent urological diseases. Identifying the causes of stone formation forms the basis for successful prevention of recurrence. Metabolic diagnostics and measures for prevention of recurrence are based on the assignment of the patient to alow-risk or high-risk group. Analysis of the urinary calculi is an essential prerequisite for identifying patients at risk. The general recommendations on diet and lifestyle are considered to be the basis of treatment. Depending on the type of stone and the individual biochemical risk profile of apatient, these general measures should be supplemented by targeted medical nutrition therapy and pharmacological treatment. Mixed stones can pose achallenge for the treatment and prevention of recurrence. Apersonalized treatment decision that takes the various components of mixed stones into account could further improve the prevention of recurrence of urolithiasis.

Predicting five-year interval second breast cancer risk in women with prior breast cancer.

Annual surveillance mammography is recommended for women with a personal history of breast cancer. Risk prediction models that estimate mammography failures such as interval second breast cancers could help to tailor surveillance imaging regimens to women's individual risk profiles. In a cohort of women with a history of breast cancer receiving surveillance mammography in the Breast Cancer Surveillance Consortium in 1996-2019, we used LASSO-penalized regression to estimate the probability of an interval second cancer (invasive cancer or ductal carcinoma in situ) in the one-year following a negative surveillance mammogram. Based on predicted risks from this one-year risk model, we generated cumulative risks of an interval second cancer for the five-year period following each mammogram. Model performance was evaluated using cross-validation in the overall cohort and within race and ethnicity strata. In 173,290 surveillance mammograms, we observed 496 interval cancers. One-year risk models were well-calibrated (expected/observed ratio = 1.00) with good accuracy (area under the receiver operating characteristic curve = 0.64). Model performance was similar across race and ethnicity groups. The median five-year cumulative risk was 1.20% (interquartile range 0.93-1.63%). Median five-year risks were highest in women who were under age 40 or pre- or peri-menopausal at diagnosis and those with estrogen receptor-negative primary breast cancers. Our risk model identified women at high risk of interval second breast cancers who may benefit from additional surveillance imaging modalities. Risk models should be evaluated to determine if risk-guided supplemental surveillance imaging improves early detection and decreases surveillance failures.

A human-centered perspective on individual risks for digital innovation management: an integrative conceptual review

PurposeFollowing the imperative for human-centric digital innovation brought by the paradigm of Industry 5.0, the article aims to integrate the dispersed and multi-disciplinary literature on individual risks for workers to define, explain and predict individual risks related to Industry 4.0 technologies.Design/methodology/approachThe paper follows the question, “What is the current knowledge and evidence base concerning risks related to Industry 4.0 technologies, and how can this inform digital innovation management in the manufacturing sector through the lens of the Industry 5.0 paradigm?” and uses the method of systematic literature review to identify and discuss potential risks for individuals associated with digital innovation. N = 51 contributions met the inclusion criteria.FindingsThe literature review indicates dominant trends and significant gaps in understanding risks from a human-centric perspective. The paper identifies individual risks, their interplay with different technologies and their antecedents at the social, organizational and individual levels. Despite this, the paper shows how the literature concentrates in studying risks on only a limited number of categories and/or concepts. Moreover, there is a lack of consensus in the theoretical and conceptual frameworks. The paper concludes by illustrating an initial understanding of digital innovation via a human-centered perspective on psychological risks.Practical implicationsFindings yield practical implications. In investing in the adoption, generation or recombination of new digital technologies in organizations, the paper recommends managers ensure to prevent risks at the individual level. Accordingly, the study’s findings can be used as a common starting point for extending the repertoire of managerial practices and interventions and realizing human-centric innovation.Originality/valueFollowing the paradigm of Industry 5.0, the paper offers a holistic view of risks that incorporates the central role of the worker as crucial to the success of digital innovation. This human-centric perspective serves to inform the managerial field about important factors in risk management that can result in more effective targeted interventions in risk mitigation approaches. Lastly, it can serve to reinterpret digital innovation management and propose future avenues of research on risk.

GLP-1 receptor agonist also reduces cardiovascular risk in individuals without diabetes

GLP-1 receptor agonist also reduces cardiovascular risk in individuals without diabetes

Potential Ecological Risk Assessment of Soil Heavy Metals in Fengdong New District Based on Information Diffution Model

The large-scale construction of new districts has led to severe soil heavy metal pollution. Therefore, taking Fengdong New District as the target research area, the descriptive statistics of heavy metal content characteristics and Kriging interpolation analysis have been conducted, and the potential ecological risk index and information diffusion theory were further combined to create an information diffusion model based on risk assessment. Finally, the pollution degree, ecological risk, and risk occurrence probability of Pb, Cu, Cd, and Hg were discussed. The findings revealed that the average concentrations of the four heavy metals far exceeded the background value of soil heavy metals by a factor of 1.943 (Pb), 1.419 (Cu), 3.074 (Cd), and 3.567 (Hg), respectively. Moreover, the distribution of soil heavy metals showed strong variability(CV>65%)owing to human interference. The distribution of Pb and Cu pollution were predominantly influenced by industrial production and land development for construction purposes, whereas industrial activities, agricultural practices, and transportation served as the primary sources of Cd contamination. On the other hand, industrial construction emerged as the major factor contributing to Hg pollution. The average values of individual potential ecological risk index for heavy metals of 9.716 (Pb), 7.095 (Cu), 92.292 (Cd), and 142.469 (Hg), coupled with the regional comprehensive potential ecological risk index (RI) average of 251.573, signified that the region was overall characterized by a relatively high potential ecological risk status. The overall potential ecological risk for Pb and Cu in the region were mild, whereas Cd and Hg posed moderate to high risks, indicating that Cd and Hg were the dominant driving factors behind regional heavy metal pollution. The evaluation results of the information diffusion model based on the potential ecological risk indicated that the probability ranking of different levels of comprehensive potential ecological risk was as follows:slightly high (38.98%) > moderate (38.55%) > high (5.89%) > slight (5.15%) > extremely high (3.56%). The exceeding probabilities of potential ecological risk levels for Cd and Hg were significantly higher than those for Pb and Cu. The exceeding probability of different pollution levels of Hg were slight (94.89%), moderate (66.85%), slightly high (23.62%), high (3.9%), and extremely high (2%), of which only the surpassing probability of the slight level was lower than that of Cd. The prediction error of pollution probability of each potential ecological risk level was less than 5%, demonstrating the reliability of the information diffusion model based on the risk assessment. This research will provide technical reference and support for the monitoring and management of potential ecological risks from soil heavy metals in limited sample data regions.

Transfer learning-enabled classification of drugs for Alzheimers Disease

Alzheimers disease (AD) is a neurodegenerative disease with a worldwide prevalence of 760.5 per 100,000 inhabitants. Environment factors and genetic predispositions can influence an individuals risk of developing AD. Recent research provided insights into the genetic mechanisms of AD, but a comprehensive database of drug-like molecules that can exacerbate or reduce AD risk remains unavailable. Here, we use machine learning to create a similarity map that reveals new putative drugs structurally similar to existing compounds known to be targeting pathways relevant in AD. We trained an autoencoder on a large drug database of over 14,000 drugs, with features derived from several modalities including molecular fingerprints. We then computed similarity scores based on these reduced dimensions. We show that our model is able to identify new compounds structurally similar to existing drugs linked to AD. We conclude that our model holds the potential to elucidate new compounds based on structural similarity and can be used to identify new drugs that affect critical pathways in AD.

Prediction of adverse neonatal adaptation in fetuses with severe fetal growth restriction after 34 weeks of gestation

ObjectiveTo establish a predictive model for adverse immediate neonatal adaptation (INA) in fetuses with suspected severe fetal growth restriction (FGR) after 34 gestational weeks (GW). Methods. We conducted a retrospective observational study at the University Hospitals of Strasbourg between 2000 and 2020, including 1,220 women with a singleton pregnancy and suspicion of severe FGR who delivered from 34 GW. The primary outcome (composite) was INA defined as Apgar 5-minute score <7, arterial pH <7.10, immediate transfer to pediatrics, or the need for resuscitation at birth. We developed and tested a logistic regression predictive model. Results. Adverse INA occurred in 316 deliveries. The model included six features available before labor: parity, gestational age, diabetes, middle cerebral artery Doppler, cerebral-placental inversion, onset of labor. The model could predict individual risk of adverse INA with confidence interval at 95 %. Taking an optimal cutoff threshold of 32 %, performances were: sensitivity 66 %; specificity 83 %; positive and negative predictive values 60 % and 87 % respectively, and area under the curve 78 %. Discussion. The predictive model showed good performances and a proof of concept that INA could be predicted with pre-labor characteristics, and needs to be investigated further.

Primary biliary cholangitis-response criteria of first-line treatment and perspectives of second-line therapy

Primary biliary cholangitis (PBC) is achronic cholestatic liver disease that can progress to liver cirrhosis if left untreated. Early diagnosis, initiation of therapy and, if necessary, adjustment of treatment are essential to prevent disease progression. The timing and thresholds for assessing adequate treatment response are inconsistently defined in the literature and can pose achallenge in clinical practice. In addition to providing aconcise overview of the guideline-based diagnostic work-up and first-line therapy, this study offers practical guidance for the evaluation of treatment response and options for second-line treatment in PBC. This article is based on the current European Association for the Study of the Liver (EASL) clinical practice guidelines for the management of PBC from 2017 as well as aliterature review of studies from 2017 to 2023, focusing on defining treatment response, assessing disease progression risk, and the approved and investigational agents for second-line therapy. There are varying definitions for asufficient response to ursodeoxycholic acid (UDCA). Therapeutic goals are tailored to the individual risk of disease progression. The lowest risk appears to be associated with normalization of alkaline phosphatase (AP) and serum bilirubin below 0.6 the upper limit of normal. Established second-line therapies include obeticholic acid and bezafibrate (off-label use), while other peroxisome proliferator-activated receptor (PPAR) agonists and combination therapies are under clinical investigation. Early evaluation of treatment response to UDCA is mandatory. In the case of insufficient treatment response, second-line therapy should be initiated according to the individual's risk profile.

Association of PNPLA3, TM6SF2 and HSD17B13 variants with NAFLD risk in HIV and non-HIV individuals

Association of PNPLA3, TM6SF2 and HSD17B13 variants with NAFLD risk in HIV and non-HIV individuals

Importance about use of high-throughput sequencing in pediatric: case report of a patient with Fanconi-Bickel syndrome

BackgroundFanconi-Bickel syndrome is characterized by hepatorenal disease caused by anomalous glycogen storage. It occurs due to variants in the SLC2A2 gene. We present a male patient of 2 years 7 months old, with failure to thrive, hepatomegaly, metabolic acidosis, hypophosphatemia, hypokalemia, hyperlactatemia.ResultsExome sequencing identified the homozygous pathogenic variant NM_000340.2(SLC2A2):c.1093 C > T (p.Arg365Ter), related with Fanconi-Bickel syndrome. He received treatment with bicarbonate, amlodipine, sodium citrate and citric acid solution, enalapril, alendronate and zolendronate, and nutritional management with uncooked cornstarch, resulting in an improvement of one standard deviation in weight and height.ConclusionsThe importance of knowing the etiology in rare genetic disease is essential, not only to determine individual and familial recurrence risk, but also to establish the treatment and prognosis; in this sense, access to a new genomic technology in low- and middle-income countries is essential to shorten the diagnostic odyssey.

Exploring the Commuting Decision-Making Behavior of Low-Income Groups Based on Cumulative Prospect Theory

Based on cumulative prospect theory, this study proposes a decision-making model to reveal the commuting behavior of low-income groups under uncertain conditions. Considering the commuting characteristics of this group, travel economic cost is incorporated into the model. The decision-making behavior of low-income groups under different travel scenarios is analyzed to acquire the priority of commuting plans. Based on a stated preference survey of residents in Beijing, the commuting behavior of low-income groups is empirically analyzed. The results show that low-income groups will be affected by individual risk preference and characterized by bounded rational characteristics when making commuting decisions under uncertain conditions. Moreover, travel economic cost is a crucial factor for the decision-making of this group. When the travel budget time is less than the perceived time, they tend to seek risks and take punctual arrival as their primary goal. On the contrary, they tend to avoid risks. On the basis of ensuring the travel time meets their needs, low-income commuters always seek the economy and reliability of their travel plans. The empirical results further verify that the proposed model effectively describes the commuting decision-making behavior of low-income groups, and it is superior to the expected utility theory with respect to performance and behavior disclosure. This study provides a reference for improving the commuting efficiency and experience of low-income groups.

Relationship Between Social Support and Clinical Outcomes: An Evaluation of Participant-Nominated Treatment Supporters in the HOPE 4 Intervention.

The HOPE 4 trial (Heart Outcomes Prevention and Evaluation 4) investigated the effectiveness of a comprehensive, collaborative model of care, implemented in Colombia and Malaysia, which aimed to reduce cardiovascular disease risk in individuals with hypertension. One component of this intervention was the nomination of a treatment supporter, where participants could select a family member or friend to assist them with their care. The purpose of this study was to investigate the impact of these individuals on participant outcomes, as well as the relationship dynamics between participants and their treatment supporter. Participants in the HOPE 4 intervention group with baseline and 12 months of follow-up were included for analysis. They were divided into Every Visit (n=339) and <Every Visit (n=268) groups based on whether they had a treatment supporter for all 5 or for <5 follow-up visits, respectively. Outcomes were stratified between groups and tested for significance using a generalized linear mixed-effects model. A survey investigating participant satisfaction with their treatment supporter was administered at 12 months. Groups were majority female (53% versus 62%) with a mean age of 63 and 66 years. Country of origin differed between groups (22% versus 86%; Colombia). A 15.5% ([95% CI, 6.2%-24.8%] P=0.004) greater increase in statin medication use was reported in the Every Visit group at 12 months compared with the <Every Visit group. Sixty-one percent versus 48.2% of participants reported high medication adherence at 12 months (P<0.003). The difference in change in systolic blood pressure between groups was not found to be significant at 12 months, though it favored the Every Visit group (-2.3 [95% CI, -6.1 to 1.5]; P=0.045). The majority of survey respondents from either study group strongly agreed that having a treatment supporter positively influenced their health. Long-term support from a nominated treatment supporter was associated with improved adherence, risk factor management, and medication use among individuals with hypertension. URL: https://www.clinicaltrials.gov; Unique identifier: NCT01826019.

Financial risk tolerance profiling from text

Traditionally, individual financial risk tolerance information is gathered via questionnaires or similar structured psychometric tools. Our abundant digital footprint, as an unstructured alternative, is less investigated. Leveraging such information can potentially support large-scale and cost-efficient financial services. Therefore, I explore the possibility of building a computational model that distills risk tolerance information from user texts in this study, and discuss the design principles discovered from empirical results and their implications. Specifically, a new quaternary classification task is defined for text mining-based risk profiling. Experiments show that pre-trained large language models set a baseline micro-F1 of circa 0.34. Using a convolutional neural network (CNN), the reported system achieves a micro-F1 of circa 0.51, which significantly outperforms the baselines, and is a circa 4% further improvement over the standard CNN configurations (micro-F1 of circa 0.47). Textual feature richness and supervised learning are found to be the key contributors to model performances, while other machine learning strategies suggested by previous research (data augmentation and multi-tasking) are less effective. The findings confirm user texts to be a useful risk profiling resource and provide several insights on this task.

Social determinants of health, the microbiome, and surgical injury.

Postinjury infection continues to plague trauma and emergency surgery patients fortunate enough to survive the initial injury. Rapid response systems, massive transfusion protocols, and the development of level 1 trauma centers, among others, have improved the outcome for millions of patients worldwide. Nonetheless, despite this excellent initial care, patients still remain vulnerable to postinjury infections that can result in organ failure, prolonged critical illness, and even death. While risk factors have been identified (degree of injury, blood loss, time to definitive care, immunocompromise, etc.), they remain probabilistic, not deterministic, and do not explain outcome variability at the individual case level. Here, we assert that analysis of the social determinants of health, as reflected in the patient's microbiome composition (i.e., community structure, membership) and function (metabolomic output), may offer a "window" with which to define individual variability following traumatic injury. Given emerging knowledge in the field, a more comprehensive evaluation of biomarkers within the patient's microbiome, from stool-based microbial metabolites to those in plasma and those present in exhaled breath, when coupled with clinical metadata and machine learning, could lead to a more deterministic assessment of an individual's risk for a poor outcome and those factors that are modifiable. The aim of this piece is to examine how measurable elements of the social determinants of health and the life history of the patient may be buried within the ecologic memory of the gut microbiome. Here we posit that interrogation of the gut microbiome in this manner may be used to inform novel approaches to drive recovery following a surgical injury.

Abstract A040: Improving specificity for ovarian cancer screening using a novel extracellular vesicle-based blood test

Abstract Introduction: The low incidence of ovarian cancer (OC) in average risk individuals dictates any OC screening method needs to be both highly sensitive and highly specific. Even using the multimodal OC screening strategy combining longitudinal serum CA125 and transvaginal ultrasound, 16% of OCs were not detected and false positives due to benign ovarian tumors, which are 5-10 times more prevalent than OC, remained high. Due to lack of mortality benefit, population screening is still not recommended. We hypothesized that detecting multiple colocalized protein or glycosylation epitopes (PGEs) on single tumor-derived extracellular vesicles (EVs) would increase specificity and sensitivity for detection of OC. Methods: The Mercy Halo™ OC test employs immunoaffinity capture and proximity-ligation qPCR to detect combinations of up to 3 PGE biomarkers to maximize specificity and measures multiple combinations to maximize sensitivity. We selected 3 PGE combinations to form a panel test to distinguish high-grade serous ovarian cancer (HGSC) from benign ovarian tumors and normal patients and compared performance of our test to CA125. We used a case-control training set from university-associated Canadian biobanks comprised of EDTA plasma from 124 healthy controls, 89 HGSC cases (17 Stage I, 35 Stage II, 37 Stage III)(10 BRCA+), and 192 benign adnexal masses to lock down the test, the data interpretation algorithm, and the cut-off between cancer and non-cancer. Performance was verified in an independent blinded diagnostic case-control set of 397 serum samples from 138 healthy controls, 111 benign adnexal masses, 20 borderlines, 66 HGSC and 62 non-HGSC from the UK Ovarian Cancer Population Study. For comparison, a CA125 ELISA was run for all samples in both studies. Results: Using the locked assay, algorithm, and cut-off established in the training cohort, our test showed a specificity in healthy controls of 92.8% (128/138; 95% CI: 0.87-0.96), a sensitivity in HGSC of 97.0% (64/66; 95% CI: 0.90-0.99), and an AUC of 0.92 (95% CI 0.89-0.95) in the verification cohort. We correctly identified 11/13 Stage I and all 54 Stage II/III/IV HGSC cases. Our test also detected 76.8% (63/82) of the borderline and non-HGSC histotypes. CA125 detected 92.4% (61/66) of the HGSC cases and 84.1% (69/82) of the borderlines and non-HGSC. The greatest improvement in test performance relative to CA125 was seen with benign adnexal masses. In the verification cohort 35.1% (39/111) of the benign samples were CA125 positive compared to 21.6% (24/111) with our test. This reduction in false positives was seen across 10 benign histotypes tested. Conclusions: These results demonstrate that our test using colocalized PGEs on EVs can detect OC, especially HGSC, in both plasma and serum with high sensitivity and specificity. The combinations of 5 biomarkers rather than CA125 alone also decreased benign false positives. The test performance suggests that our test may be useful in average and/or high-risk ovarian cancer screening. Future studies will explore this in asymptomatic populations. Citation Format: Emily S. Winn-Deen, Sanchari Banerjee, Daniel Gusenleitner, Laura T. Bortolin, Jonian Grosha, Kelly M. Biette, Karen Copeland, Christopher R. Sedlak, Bilal Hamzeh, Anthony D. Couvillon, Delaney M. Byrne, Peter A. Duff, Lauren T. Cuoco, MacKenzie S. King, Aleksandra Gentry-Maharaj, Sophia Apostolidou, Dawn R. Mattoon, Christine D. Berg, David F. Ransohoff, Steven J. Skates, Usha Menon. Improving specificity for ovarian cancer screening using a novel extracellular vesicle-based blood test [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A040.

Assessment of color changes and adverse effects of over-the-counter bleaching protocols: a systematic review and network meta-analysis.

To assess color change efficacy and the adverse effects of varied over-the-counter (OTC) bleaching protocols. The study included randomized clinical trials evaluating color changes from OTC bleaching agents. Nine databases were searched, including the partial capture of the grey literature. The RoB2 tool analyzed the individual risk of bias in the studies. Frequentist network meta-analyses compared treatments through common comparators (∆Eab* and ∆SGU color changes, and tooth sensitivity), integrating direct and indirect estimates and using the mean and risk differences as effect measures with respective 95% confidence intervals. The GRADE approach assessed the certainty of the evidence. Overall, 37 remaining studies constituted the qualitative analysis, and ten composed the meta-analyses. The total sample included 1,932 individuals. ∆Eab* was significantly higher in groups 6% hydrogen peroxide (HP) strips (≥ 14h). ∆SGU was significantly higher in groups at-home 10% carbamide peroxide (CP) (≥ 14h), followed by 6% HP strips (≥ 14h) and 3% HP strips (≥ 14h). At-home 10% CP (7-13h) and placebo showed lower risks of tooth sensitivity without significant differences between these treatments. Considering the low level of evidence, OTC products presented satisfactory short-term effects on tooth bleaching compared to the placebo, with little to no impact on dentin hypersensitivity and gingival irritation. OTC products are proving to be practical alternatives for tooth whitening. However, patients should be advised about the possible risks of carrying out such procedures without professional supervision.

Clinical Characteristics of Asymptomatic Thromboembolism in Psychiatric Inpatients: A Retrospective Study.

Venous thromboembolism (VTE) poses a significant threat to individuals' health, yet its correlation with mental disorders remains underappreciated. Here, we conducted a retrospective analysis to explore the characteristics of psychiatric patients presenting with VTE. We retrospectively analyzed psychiatric inpatients with elevated plasma D-dimer levels at the Mental Health Center, First Affiliated Hospital, Zhejiang University School of Medicine, from January 2014 to January 2022. The inclusion criteria comprised comprehensive demographic and clinical profiles, including laboratory and imaging findings. A cohort of 33 eligible patients was included, with plasma D-dimer levels ranging from 880 to 10,700 μg/L FEU. Significantly higher D-dimer levels were observed in patients diagnosed with severe mental disorders (SMD), such as schizophrenia and bipolar disorder, compared to those with mild mental disorders (MMD), including depression and anxiety disorders (p = 0.007). Furthermore, individuals receiving antipsychotic medications for less than one year exhibited elevated D-dimer levels compared to those on treatment for over one year (p = 0.005). However, normalization of D-dimer levels did not demonstrate a significant association with psychiatric diagnosis or treatment duration (p > 0.05). Our findings suggest that patients diagnosed with SMD or those undergoing antipsychotic treatment for less than one year may have elevated D-dimer levels, indicating a potential predisposition to VTE severity. This underscores the importance of recognizing VTE risk in individuals with severe mental disorders and warrants further investigation into the impact of antipsychotic treatment duration on thrombotic risk.