Tag: pubmed

Med Pr. 2021 Aug 31;72(4):363-373. doi: 10.13075/mp.5893.01114. Epub 2021 Aug 27.

ABSTRACT

BACKGROUND: Physical therapists are known to be susceptible to work-related musculoskeletal disorders (WMSDs), but the prevalence of WMSDs in Saudi Arabia has not been documented. This study aimed to establish the prevalence, characteristics, and risk factors of WMSDs among physical therapists in Saudi Arabia.

MATERIAL AND METHODS: A cross-sectional study was conducted among 113 physical therapists in Saudi Arabia using a 6-component questionnaire. Descriptive statistics, incidence, percentages, and χ² test were used for data analysis.

RESULTS: The response rate was 68.8%. The reported 12-month incidence of WMSDs was 83.8%. The low back (63.7%) was the most common site of these disorders, followed by the neck (59.2%), while the hip/thigh (4.4%) was the least involved body part. Incidence was related to gender: females were more affected than males (neck, shoulders, low back); age: younger therapists were more affected than older ones (shoulders, low back); working sector: government sector workers were more affected than those employed in other sectors (neck); and specialty: orthopedic specialists were the most frequently affected, followed by those specializing in neurology (thumbs, upper back, knees, ankle/foot). Most of the physical therapists had >5 periods of neck, shoulder, and low-back WMSDs. The most important risk factor for WMSDs was treating more patients in a day (47.7%). The most frequently adopted handling strategy identified to combat WMSDS was modifying the patient’s position (62.8%).

CONCLUSIONS: Overall, WMSDs among physical therapists in Saudi Arabia are common, with the low back and the neck constituting the most frequently affected body regions. Professional experience and the awareness of ergonomics principles can help prevent the early development of WMSDs among physical therapists. Med Pr. 2021;72(4):363-73.

PMID:34467955 | DOI:10.13075/mp.5893.01114

Sci Total Environ. 2021 Aug 8;801:149342. doi: 10.1016/j.scitotenv.2021.149342. Online ahead of print.

ABSTRACT

Agriculture soils are responsible for a large proportion of global nitrous oxide (N₂O) emissions-a potent greenhouse gas and ozone depleting substance. Enhanced-efficiency nitrogen (N) fertilizers (EENFs) can reduce N₂O emission from N-fertilized soils, but their effect varies considerably due to a combination of factors, including climatic conditions, edaphic characteristics and management practices. In this study, we further developed the DayCent ecosystem model to simulate two EENFs: controlled-release N fertilizers (CRNFs) and nitrification inhibitors (NIs) and evaluated their N₂O mitigation potentials. We implemented a Bayesian calibration method using the sampling importance resampling (SIR) algorithm to derive a joint posterior distribution of model parameters that was informed by N₂O flux measurements from corn production systems a network of experimental sites within the GRACEnet program. The joint posterior distribution can be applied to estimate predictions of N₂O reduction factors when EENFs are adopted in place of conventional urea-based N fertilizer. The resulting median reduction factors were – 11.9% for CRNFs (ranging from -51.7% and 0.58%) and – 26.7% for NIs (ranging from -61.8% to 3.1%), which is comparable to the measured reduction factors in the dataset. By incorporating EENFs, the DayCent ecosystem model is able to simulate a broader suite of options to identify best management practices for reducing N₂O emissions.

PMID:34467931 | DOI:10.1016/j.scitotenv.2021.149342

Sci Total Environ. 2021 Aug 19;801:149757. doi: 10.1016/j.scitotenv.2021.149757. Online ahead of print.

ABSTRACT

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 129 million confirm cases. Many health authorities around the world have implemented wastewater-based epidemiology as a rapid and complementary tool for the COVID-19 surveillance system and more recently for variants of concern emergence tracking. In this study, three SARS-CoV-2 target genes (N1 and N2 gene regions, and E gene) were quantified from wastewater influent samples (n = 250) obtained from the capital city and 7 other cities in various size in central Ohio from July 2020 to January 2021. To determine human-specific fecal strength in wastewater samples more accurately, two human fecal viruses (PMMoV and crAssphage) were quantified to normalize the SARS-CoV-2 gene concentrations in wastewater. To estimate the trend of new case numbers from SARS-CoV-2 gene levels, different statistical models were built and evaluated. From the longitudinal data, SARS-CoV-2 gene concentrations in wastewater strongly correlated with daily new confirmed COVID-19 cases (average Spearman’s r = 0.70, p < 0.05), with the N2 gene region being the best predictor of the trend of confirmed cases. Moreover, average daily case numbers can help reduce the noise and variation from the clinical data. Among the models tested, the quadratic polynomial model performed best in correlating and predicting COVID-19 cases from the wastewater surveillance data, which can be used to track the effectiveness of vaccination in the later stage of the pandemic. Interestingly, neither of the normalization methods using PMMoV or crAssphage significantly enhanced the correlation with new case numbers, nor improved the estimation models. Viral sequencing showed that shifts in strain-defining variants of SARS-CoV-2 in wastewater samples matched those in clinical isolates from the same time periods. The findings from this study support that wastewater surveillance is effective in COVID-19 trend tracking and provide sentinel warning of variant emergence and transmission within various types of communities.

PMID:34467932 | DOI:10.1016/j.scitotenv.2021.149757

Sci Total Environ. 2021 Aug 18;801:149743. doi: 10.1016/j.scitotenv.2021.149743. Online ahead of print.

ABSTRACT

The diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection requires the detection of viral RNA by reverse transcription-polymerase chain reaction (RT-qPCR) performed mainly using nasopharyngeal swabs. However, this procedure requires separate analysis per each individual, performed in advanced centralized laboratory facilities with specialized medical personnel. In this study, an alternative approach termed “solid waste-based surveillance (SWBS)” was explored, in order to investigate SARS-CoV-2 infection in small communities through the indirect sampling of saliva left on waste. Sampling was performed at 20 different sites in Italy during the second peak of COVID-19. Three swabs were positive for SARS-CoV-2 using a published RT-qPCR protocol targeting the non-structural protein 14 region, and the viral load ranged 4.8 × 10³-4.0 × 10⁶ genome copies/swab. Amino acid substitutions already reported in SARS-CoV-2 sequences circulating in Italy (A222V and P521S) were detected in two positive samples. These findings confirmed the effectiveness of SWBS for non-invasive and dynamic SARS-CoV-2 surveillance.

PMID:34467913 | DOI:10.1016/j.scitotenv.2021.149743

Sci Total Environ. 2021 Aug 21;801:149797. doi: 10.1016/j.scitotenv.2021.149797. Online ahead of print.

ABSTRACT

Accurate prediction of any type of natural hazard is a challenging task. Of all the various hazards, drought prediction is challenging as it lacks a universal definition and is getting adverse with climate change impacting drought events both spatially and temporally. The problem becomes more complex as drought occurrence is dependent on a multitude of factors ranging from hydro-meteorological to climatic variables. A paradigm shift happened in this field when it was found that the inclusion of climatic variables in the data-driven prediction model improves the accuracy. However, this understanding has been primarily using statistical metrics used to measure the model accuracy. The present work tries to explore this finding using an explainable artificial intelligence (XAI) model. The explainable deep learning model development and comparative analysis were performed using known understandings drawn from physical-based models. The work also tries to explore how the model achieves specific results at different spatio-temporal intervals, enabling us to understand the local interactions among the predictors for different drought conditions and drought periods. The drought index used in the study is Standard Precipitation Index (SPI) at 12 month scales applied for five different regions in New South Wales, Australia, with the explainable algorithm being SHapley Additive exPlanations (SHAP). The conclusions drawn from SHAP plots depict the importance of climatic variables at a monthly scale and varying ranges of annual scale. We observe that the results obtained from SHAP align with the physical model interpretations, thus suggesting the need to add climatic variables as predictors in the prediction model.

PMID:34467917 | DOI:10.1016/j.scitotenv.2021.149797

Eur J Obstet Gynecol Reprod Biol. 2021 Aug 9;265:74-79. doi: 10.1016/j.ejogrb.2021.08.010. Online ahead of print.

ABSTRACT

OBJECTIVE: Uterine smooth muscle tumors of uncertain malignant potential (STUMP) is a heterogeneous group of tumors with histological and biological diversity that cannot be defined as a benign leiomyoma or malignant leiomyosarcoma. The study aims to investigate the diagnostic methods, treatment management and prognosis of STUMP patients in a 13-year period.

STUDY DESIGN: We retrospectively reviewed the clinicopathologic information of 31 STUMP patients in Peking University People’s Hospital. Statistical analyses were conducted to compare the difference of clinical characteristics between the women in myomectomy group and those in hysterectomy group.

RESULTS: The most common clinical presentation was menstrual disorder. The tumors were mainly manifested as hypoechoic, non-cystic nodules with low blood flow signal by pelvic doppler ultrasonography. Most tumors carried Ki-67 index ranging from 10% to 30%. Immunohistochemical markers such as ER, PR, p16 and Desmin was positively expressed in tumors. At the first operation, 21 cases underwent myomectomy and 10 cases underwent hysterectomy. The patients in myomectomy group were younger than those in hysterectomy group. In the follow-up period, two cases experienced a relapse in the form of STUMP within 36 months. One case died of cardiovascular accident while the other cases were alive. Six of 21 women in myomectomy group desired pregnancy and two healthy live births were recorded.

CONCLUSION: The diagnosis of STUMP primarily depends on histopathologic features. Fertility-sparing surgery may be a treatment selection for patients with fertility desire. Patients with STUMP, especially in the case of myomectomy, should be informed of recurrence risk and monitored closely.

PMID:34467879 | DOI:10.1016/j.ejogrb.2021.08.010

Sci Total Environ. 2021 Aug 18;801:149693. doi: 10.1016/j.scitotenv.2021.149693. Online ahead of print.

ABSTRACT

Appropriate produced water (PW) management is critical for oil and gas industry. Understanding PW quantity and quality trends for one well or all similar wells in one region would significantly assist operators, regulators, and water treatment/disposal companies in optimizing PW management. In this research, historical PW quantity and quality data in the New Mexico portion (NM) of the Permian Basin from 1995 to 2019 was collected, pre-processed, and analyzed to understand the distribution, trend and characteristics of PW production for potential beneficial use. Various machine learning algorithms were applied to predict PW quantity for different types of oil and gas wells. Both linear and non-linear regression approaches were used to conduct the analysis. The prediction results from five-fold cross-validation showed that the Random Forest Regression model reported high prediction accuracy. The AutoRegressive Integrated Moving Average model showed good results for predicting PW volume in time series. The water quality analysis results showed that the PW samples from the Delaware and Artesia Formations (mostly from conventional wells) had the highest and the lowest average total dissolved solids concentrations of 194,535 mg/L and 100,036 mg/L, respectively. This study is the first research that comprehensively analyzed and predicted PW quantity and quality in the NM-Permian Basin. The results can be used to develop a geospatial metrics analysis or facilitate system modeling to identify the potential opportunities and challenges of PW management alternatives within and outside oil and gas industry. The machine learning techniques developed in this study are generic and can be applied to other basins to predict PW quantity and quality.

PMID:34467907 | DOI:10.1016/j.scitotenv.2021.149693

Pediatr Dent. 2021 Jul 15;43(4):276-281.

ABSTRACT

Purpose: Childhood caries is a highly prevalent disease that is intricately connected to diet and other social and behavioral factors. While it has been established that breastfeeding confers many health benefits for children, previous research found no consensus on the relationship between breastfeeding and caries. The purpose of this study was to examine the relationship between early childhood caries (ECC) and the length of time breastfeeding using the National Health and Nutrition Examination Survey (NHANES). Methods: Four cycles of NHANES (2011 to 2018) were analyzed, including 3,234 children ages two to five years. The association between breastfeeding duration and incidence of ECC and severe earlychildhood caries (S-ECC) was evaluated using logistic regression, adjusting for age, ethnicity, education, income, last dental visit, and sugar-sweetened beverages. Results: In the study population, 16.9 percent had ECC and 12.2 percent had S-ECC. Breastfeeding six months to one year, one to two years, or over two years was not associated with higher odds of ECC or S-ECC than breastfeeding for zero to six months after adjusting for covariates. Conclusions: There was no statistically significant relationship between breastfeeding and early childhood caries, and breastfeeding duration was not associated with increased caries risk. More research from well-controlled analytical studies is needed to establish or refute a relationship between breastfeeding and ECC.

PMID:34467843

Pediatr Dent. 2021 Jul 15;43(4):290-295.

ABSTRACT

Purpose: The purpose of this study was to evaluate the treatment outcomes of multisurface caries in primary molars treated with intracoronal restorations versus stainless steel crowns (SSCs) through a retrospective split-mouth study. Methods: Dental records were screened for patients who had treatment of one primary molar with a multisurface restoration and one primary molar with an SSC. Teeth were followed until a loss to follow-up, exfoliation, or failure. Results: A total of 988 primary molars were evaluated, with a mean follow-up time of 22 months. The survival probabilities for: SSCs were 95.5 percent at one year of service and 92.8 percent at two years of service; and for intracoronal restorations were 92.0 percent at one year of service and 80.0 percent at two years of service. Overall survival analysis showed SSCs to be significantly more successful than restorations (P<0.001), particularly in children treated at ages four years and younger (P<0.001). No statistically significant difference (P=0.10) was found for children treated at ages five years and older. Conclusions: Stainless steel crowns have a higher survival probability versus restorations for multisurface caries. In children ages four years and younger, more aggressive treatment of multi-surface caries with SSCs should be considered, as conservative treatment leads to an increased need for retreatment.

PMID:34467846

J Environ Manage. 2021 Aug 28;299:113594. doi: 10.1016/j.jenvman.2021.113594. Online ahead of print.

ABSTRACT

Nowadays, releasing the Emerging Pollutants (EPs) in the nature is one of the main reasons for many health and environmental disasters. Amoxicillin as an antibiotic is one of the EPs and categorized as the Endocrine Disrupting Compounds (EDCs) in hazardous materials. Accumulation of amoxicillin in the soil bulk increases the cancer risk, drug resistances and other epidemiological diseases. Hence, the soil bioremediation of antibiotics can be a solution for this problem which is more environmental-friendly system. This study technically creates a bio-engine setup in soil bulk for remediation of amoxicillin based on Aspergillus Flavus (AF) activities and Removal Percentage (RP) of amoxicillin with Aflatoxin B1 Generation (AG) controls. The main novelty is to propose a hybrid computational intelligence approach to do optimization for mechanical and biological aspects and to predict the behavior of bio-engine’s effective mechanical and biological features in an intelligent way. The optimization model is formulated by the Central Composite Design (CCD) which is set by the Response Surface Methodology (RSM). The prediction model is formulated by the Random Forest (RF), Adaptive Neuro Fuzzy Inference System (ANFIS) and Random Tree (RT) algorithms. According to the experimental practices from real soil samples in different times and places, concentration of amoxicillin and Aflatoxin B1 are set equal to 25 mg/L (ppm) and 15 μg/L (ppb). Likewise, the outcomes of experiments in CCD-RSM computations are evaluated by curve fitting comparisons between linear, 2FI, quadratic and cubic polynomial equations with considering to regression coefficient and predicted regression coefficient values, ANOVA and optimization by sequential differentiation. Based on the results of CCD-RSM, the RP performance in the optimum conditions is measured around 86% and in 25 days after runtime, the RP and AG are balanced in the safe mode. The proposed hybrid model achieves the 0.99 accuracy. The applicability of the research is done using real field evaluations from drug industrial park in Mashhad city in Iran. Finally, a broad analysis is done and managerial insights are concluded. The main findings of the present research are: (I) with application of bioremediation from fungus activities, amoxicillin amounts can be control in soil resources with minimum AG, (II) ANFIS model has the best accuracy for smart monitoring of amoxicillin bioremediation in soil environments and (III) based on the statistical assessments Aeration Intensity and AF/Biological Waste ratio are most effective on the amoxicillin removal percentage.

PMID:34467868 | DOI:10.1016/j.jenvman.2021.113594