BACKGROUND: Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions.
OBJECTIVE: This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support.
METHODS: We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model\'s performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support.
RESULTS: We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4.
CONCLUSIONS: When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

UNASSIGNED: Workplace violence is a significant challenge in psychiatric hospital care. Some existing practices of violence risk assessment and management are based on nurses\' intuition and clinical experience instead of structured tools.
UNASSIGNED: Nurses and nurse managers consider violence risk assessment and management their responsibility. Still, nurses and nurse managers have mixed attitudes towards the use of validated risk assessment tools. The attitudes towards service users\' positive risk-taking in nurses and nurse managers vary, with some nurses and nurse managers supporting its importance.
CONCLUSIONS: Change in nurses\' and nurse managers\' attitudes towards risk assessment tools is required before their implementation into practice. More profound change in practices towards recovery-oriented care is required also in risk assessment.
UNASSIGNED: INTRODUCTION: Workplace violence is a prevalent issue in psychiatric inpatient care. Prevention efforts require the identification of at-risk service users using validated violence risk assessment tools. The shift in violence prevention emphasises preventive measures and collaborative risk assessment together with service users. Nurses have a central role in this process. Therefore, their attitudes are crucial when implementing evidence-based methods.
OBJECTIVE: To assess the attitudes of nurses and nurse managers towards violence risk assessment and management.
METHODS: A cross-sectional online survey in Finnish psychiatric inpatient care. Data analysis was conducted with statistical methods. The STROBE guideline was used in reporting.
RESULTS: Nurses (n = 142) valued risk assessment and felt that it was their responsibility. Attitudes towards service users\' risk-taking as part of their recovery varied. Nurses had mixed attitudes regarding the effectiveness of risk assessment tools. Older participants and nurse managers had more positive attitudes towards risk assessment tools.
CONCLUSIONS: The study findings highlight a feeling of responsibility of nurses towards violence risk assessment and at the same time their preference towards their own clinical judgement.
CONCLUSIONS: Understanding nurses\' attitudes is crucial in training and implementation processes to address concerns, provide support and enhance positive attitudes.

The recently introduced Implant Disease Risk Assessment (IDRA) identifies a restoration margin-alveolar bone crest (RM-AC) distance of less than 1.5 mm as a key risk factor for peri‑implant disease among eight major risk factors. This study evaluated the impact of the RM-AC distance on marginal bone loss (MBL) through radiographic analysis.
This retrospective cross-sectional study included 77 partially edentulous patients (39 females and 38 males, aged 22 to 76 years) with 202 platform-switched conical connection implants, cement-retained, implant-supported fixed restorations, and bone-level implants placed between 2016 and 2021. Dental implants were followed for least 6 to 36 months at follow up functional loading. Study participants were categorized into Group A (RM-AC distance ≤ 1.5 mm, n = 69) and Group B (RM-AC distance > 1.5 mm, n = 133). Twelve patients in Group B and five patients in Group A had no history of periodontal disease. The MBL was measured radiographically from the most coronal point of the implant shoulder to the alveolar bone, and the RM-AC distance was measured from the restoration margin to the alveolar crest. Multinomial logistic regression analysis was used for statistical evaluation.
The incidence of MBL in Group A was statistically significant and 3.42 times higher than that in Group B. The rate of MBL in periodontitis Stage 4 was found to be 26.31 times higher than that in periodontitis Stage 2. The incidence of MBL was 6.097 and 5.02 times higher with increasing implant diameter and length, respectively.
This study conclusively demonstrates that RM-AC distance ≤ 1.5 significantly increases the risk of MBL, particularly in patients with a history of periodontal disease.
This study highlights the critical role of maintaining an RM-AC distance greater than 1.5 mm in the prevention of MBL, particularly in patients with a history of periodontal disease. Since implant diameter and length have a significant impact on the risk of MBL, it emphasizes that implant demographics should also be carefully evaluated.

Cantaloupe is a popular agricultural product in the hot season of Iran. On the other hand, the frequent use of pesticides in cantaloupe fields is the most important threat to the health of farmers and consumers. Therefore, the present study aims to measure the concentration of diazinon (DZN), chlorpyrifos (CPF), and malathion (MLT) in cantaloupe cultivated in Kashan and Aran-Bidgol (Iran) and to estimate the possible oral and dermal risk of these pesticides by Monte Carlo simulation (MCS). 36 cantaloupe samples, 18 samples before, and 18 samples after the latent period were collected from different places of cantaloupe cultivation from April to May 2021. After measuring the pesticides using the QuEChERS approach, oral and dermal risk assessments were calculated.The mean and standard deviation of the concentrations of chlorpyrifos, malathion, and diazinon in 18 cantaloupe samples, after the latent period, were (30.39 ± 13.85), (18.361 ± 1.8), and (21.97 ± 0.86) μg kg-1, respectively. Concentration of Malathion, diazinon, and Chlorpyrifos in the soil were 0.22, 0.25, and 0.3 mg kg-1, respectively, and pesticide cumulative risk assessment in soil was obtained 0.011 for Malathion, 0.05 for diazinon and 0.03 for Chlorpyrifos. Target Hazard Quotient (THQ) according to the cantaloupe consumption and dermal exposure in children and adults, was safe range. Although non-cancerous dermal and oral risk of cantaloupe is low, constant exposure can be harmful. Therefore, the findings of this study play an important role in increasing the understanding of the negative health consequences of pesticide contamination in cantaloupe for consumers, especially local residents, and can help by adopting remedial strategies to reduce environmental concerns.

Low-dose aspirin treatment reduces the risk of preeclampsia among high-risk pregnant women. Internationally, several first-trimester risk-calculation methods are applied.
This study aimed to assess the costs and benefits of different first-trimester preeclampsia risk estimation algorithms: EXPECT (an algorithmic prediction model based on maternal characteristics), National Institute for Health and Care Excellence (a checklist of risk factors), and the Fetal Medicine Foundation (a prediction model using additional uterine artery Doppler measurement and laboratory testing) models, coupled with low-dose aspirin treatment, in comparison with no risk assessment.
We constructed a decision analytical model estimating the number of cases of preeclampsia with each strategy and the costs of risk assessment for preeclampsia and early aspirin treatment, expressed in euros (€) in a hypothetical population of 100,000 women. We performed 1-way sensitivity analyses to assess the impact of adherence rates on model outcomes.
Application of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models results in respectively 1.98%, 2.55%, and 1.90% of the women developing preeclampsia, as opposed to 3.00% of women in the case of no risk assessment. Overall, the net financial benefits of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models relative to no risk assessment are €144, €43, and €38 per patient, respectively. The respective percentages of women receiving aspirin treatment are 18.6%, 10.2%, and 6.0% for the 3 risk assessment methods.
The EXPECT and Fetal Medicine Foundation model are comparable with regard to numbers of prevented preeclampsia cases, and both are superior to the National Institute for Health and Care Excellence model and to no risk assessment. EXPECT is less resource-demanding and results in the highest cost savings, but also requires the highest number of women to be treated with aspirin. When deciding which strategy is preferable, cost savings and easier use have to be weighed against the degree of overtreatment, although low-dose aspirin has no clear disadvantages during pregnancy.

Short-chain (SC) and medium-chain (MC) chlorinated paraffins (CPs) are found widely in the environment. Little research into air-soil exchange of SCCPs and MCCPs has been performed. In this study, CP concentrations, congener group profiles, and air-soil exchange in a typical contaminated area were investigated. A total of 10 soil samples and 10 air samples were collected from Zhoushan, an island in China. The samples were analyzed by two-dimensional gas chromatography electron capture negative ionization mass spectrometry. The SCCP and MCCP concentrations in the soil samples were 72-3842 and 117-8819 ng/g, respectively, and the SCCP and MCCP concentrations in the air samples were 57-208 and 1.8-25 ng/m3, respectively. The highest CP concentrations in both soil and air were found in samples from near shipyards, possibly because of CPs being emitted from metal cutting fluids and marine paints used at the shipyards. C14-15Cl7-9 were the dominant CP congener groups in the soil samples. C10Cl6-7 were the dominant CP congener groups in the air samples. Chlorinated decane and undecane and penta-, hexa-, and hepta-chlorinated CPs were enriched in the air relative to the soil. These congeners may have been released from the commercial CP-42 and CP-52. The fugacity fractions (ffs) of 48 homologs decreased as Koa increased. The ffs indicated that SCCPs and MCCPs dominated deposition. The net air-soil exchange fluxes of CPs were 201-769 ng/(m2·h). A preliminary risk assessment indicated that CPs pose low ecological risk except at sampling site S7 and do not pose significant health risks.

In this study, the climate change, tsunami and biodiversity for 336 km coastline endangered at the South China Sea was investigated with the review for the past, current and prediction models for the future. The hydraulic study of the coastal area was conducted using a well-established 2D numerical model suite Delft3D. The study revealed that the generated earthquakes at the convergence zone in the last century are small (Mw7.3), the possibility that a megathrust earthquake event in the SCS basin occurs in the future. The study area comprises a narrow strip of vegetation notably dominated by Casuarina equisetifolia with other coastal plants. Mangrove forests are found along the coastline and estuaries that are overlaid with marine alluvial soils. The current paper is the first comprehensive study of the South China Sea, and the findings increase the awareness among the public to understand the risk associated with environmental pollution.

The oil was extracted from sesame seed with two extraction methods. Traditional (Ardeh oil) and industrial method (cold pressing method: virgin and refined sesame oil) oil extraction was studied to compare the quality and heavy metal content of extracted oils. The chemical properties (fatty acid composition, peroxide, anisidine, acid values, and TOTOX) and heavy metal contents were investigated. The Hazard Quotient (HQ) and Hazard Index (HI) of heavy metal intakes were calculated. The results demonstrated that the predominant fatty acid in oil samples was oleic, linoleic, palmitic, and stearic acids. It was indicated the peroxide, anisidine, acid values, and TOTOX of oil samples were as the order of Ardeh oil > virgin sesame oil > refined sesame oil. The reduction pattern of Pb > Zn >Cu > Cd >As was reported in sesame seed. Although the oil refining had been greatly reduced the Pb of oil sample, but it had yet been much higher than the permissible levels set by Codex Alimentarius. The HQ and HI of all heavy metals were less than one, but they were higher in Ardeh oil compared to others. It is necessary to monitor the presence of heavy metal contaminants and the quality of imported sesame seeds prior to oil preparation.

Data monitoring is a prerequisite for the occurrence of organophosphate esters (OPEs) in the soil environment in light of their potential toxicity, bioaccumulation, and environmental persistence. In this study, we determined the concentrations and profiles of OPEs in surface soils collected from Jinan City, East China. The soil concentrations of ΣOPE (sum of all OPEs) were in the range of 2.55-581 ng/g dry weight (dw), with an average value of 106 ng/g dw. Industrial soils (mean: 433 ng/g dw) had significantly higher levels of ΣOPE compared with those in urban (42.1 ng/g dw) and farmland soils (7.89 ng/g dw) (p < 0.01), suggesting that industrial activity is an important source of OPEs to ambient soil environment. Tris(1-chloro-2-propyl) phosphate (TCIPP), triphenyl phosphate (TPHP), and tris(2-butoxyethyl) phosphate (TBOEP) were the most abundant OPEs in industrial soils, contributing 30%, 25%, and 20% of ΣOPE, respectively. Principal component analysis revealed that TCIPP, TPHP, and TBOEP in soils derived from respective industrial activities. As compared with other cities within China, the surface soil of Jinan City was mildly contaminated by OPEs, and its human exposure and eco-toxicological risks were found to be negligible. Our study provides current contamination status of OPEs in soils across the multiple functional regions of Jinan, which could be used to support the authorities to make relevant regulations.

Prevalence of fluorosis is a worldwide public health issue, especially in the West Azerbaijan province of Iran. The aim of this study was to investigate fluoride concentration in drinking water resources within Maku city, in both the warm and cold seasons, to perform a health risk assessment. Fluoride were measured using UV-visible spectrophotometry. The spatial distribution was calculated by the software ArcGIS and Hazard Quotients (HQs) were calculated according to the US EPA method. The fluoride concentrations ranged between 0.29 to 6.68 and 0.1 to 11.4 mg/L in the cold and warm seasons, respectively. Based on this report, 30.64 and 48.15% of the samples revealed a fluoride level higher than the permissible level in the cold and warm seasons, respectively. Moreover, results showed that the HQ value in the warm season for different age groups was higher than the HQ value in the cold season. In both seasons, the non-carcinogenic risks of fluoride for the four exposed populations varied according to the order: children > teenagers > adults > infants. The HQ values for three age groups (children, teenager and adults) for both seasons were higher than 1 with a high risk of fluorosis. The results of this study, support the requests that government authorities better manage water supplies to improve health quality.