Intravenous Paracetamol Versus Patient-Controlled Analgesia With Morphine for the Pain Management Following Diagnostic Knee Arthroscopy in Trauma Patients: A Randomized Clinical Trial

Background: Most patients undergoing outpatient surgeries have the unpleasant experience of high level pain after surgery. Compared with open surgeries, arthroscopic procedures are less painful; however, inadequate pain management could be associated with significant concerns. Opioids alone or in combination with local anesthetics are frequently used for diminishing postoperative pain using intravenous or epidural infusion pumps. Despite morphine various disadvantages, it is commonly used for controlling pain after surgery. Objectives: The aim of this study was to compare intravenous paracetamol and patient-controlled analgesia (PCA) with morphine for the pain management following diagnostic knee arthroscopy in trauma patients. Patients and Methods: Sixty trauma patients who were scheduled to undergo knee arthroscopy were randomly divided into two groups. Patients immediately received intravenous infusion of 1 g paracetamol within 15 minutes after surgery and every 6 hours to 24 hours in the paracetamol group. The patient-controlled analgesia group received morphine through PCA infusion pump at 2 mL/h base rate and 1mL bolus every 15 minutes. Pain level, nausea and vomiting, and sedation were measured and recorded during entering the recovery, 15 and 30 minutes after entering the recovery, 2, 6, and 24 hours after starting morphine pump infusion in the morphine and paracetamol in the paracetamol groups. Results: There was no significant difference regarding the pain level at different times after entering the recovery between the two groups. No one from the paracetamol group developed drug complications. However, 22.3% in the PCA morphine suffered from postoperative nausea; there was a statistically significant difference regarding the sedation level, nausea, and vomiting at various times between the two groups. Conclusions: Intravenous administration of paracetamol immediately after knee arthroscopy improved postoperative pain, decreased analgesic administration, maintained stable hemodynamic parameters, had no complications related to opiates, no nausea and vomiting, and increased patient satisfaction and comfort in comparison to PCA with morphine

Parenting interventions for male young offenders: a review of the evidence on what works

Approximately one in four incarcerated male young offenders in the UK is an actual or expectant father. This paper reviews evidence on the effectiveness of parenting interventions for male young offenders. We conducted systematic searches across 20 databases and consulted experts. Twelve relevant evaluations were identified: 10 from the UK, of programmes for incarcerated young offenders, and two from the US, of programmes for young parolees. None used experimental methods or included a comparison group. They suggest that participants like the courses, find them useful, and the interventions may improve knowledge about, and attitudes to, parenting. Future interventions should incorporate elements of promising parenting interventions with young fathers in the community, for example, and/or with older incarcerated parents. Young offender fathers have specific developmental, rehabilitative, and contextual needs. Future evaluations should collect longer-term behavioural parent and child outcome data and should use comparison groups and, ideally, randomization

Spatial modelling of soil salinity: deep or shallow learning models?

Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks—DCNNs, dense connected deep neural networks—DenseDNNs, recurrent neural networks-long short-term memory—RNN-LSTM and recurrent neural networks-gated recurrent unit—RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree—BCART, cforest, cubist, quantile regression with LASSO penalty—QR-LASSO, ridge regression—RR and support vectore machine—SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0–5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE ≥ 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE ≤ 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences

Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran

Wind erosion have many negative effects on global terrestrial and aquatic ecosystems and these phenomena are controlled by several factors including climatic, meteorological, topographic, vegetation, surface and soil characteristics. This study applied, for the first time, the Boruta algorithm for identification of effective variables controlling wind erosion. The novelty of the study was increased further using application of two deep learning (DL) models comprising a simple recurrent neural network (RNN) and restricted boltzmann machine (RBM). Collectively, these tools were used to map land susceptibility to wind erosion in parts of Kerman province, southeastern Iran. Among 18 potential variables for controlling dust emissions via wind erosion, 4 and 14 were identified as non-important and important, respectively, by the Boruta algorithm, while three (precipitation, digital elevation model and soil organic carbon) were selected as the most important factors. An inventory map of the wind erosion confirmed using both a test dataset (30%) and a training dataset (70%) was used to construct predictive models of land susceptibility to wind erosion. Both DL predictive models exhibited highly satisfactory performance according to a Taylor diagram, but the simple RNN performed slightly better than RBM. Based on the simple RNN, 35.6%, 5%, 2.4%, 22.7% and 34.3% of the total study area were characterized by very low, low, moderate, high and very high susceptibility, respectively. Convergent prediction of the same susceptibility classes by intersecting the maps generated by both models classified 17.4%, 0.07%, 0.06%, 7.4% and 34% of the total study area as very low, low, moderate, high and very high susceptibility classes, respectively. We conclude that applying the Boruta algorithm and DL models as new methods in aeolian geomorphology, may provide accurate spatial maps of dust sources to help target mitigation of detrimental dust effects on climate, ecosystems and human health

CERVICAL ERECTOR SPINAE INTERMUSCULAR COORDINATIONWHILE USING NOISE CANCELLATION HEADPHONES DURING WALKING

Kia Golzari1, Ali Boolani2, Zacharias Papadakis3, Sergi Garcia-Retortillo4, Andreas Stamatis, FACSM5, Emily Locke2, Ryan McCarthy2, Kwadwo Osei Appiah-Kubi2, Ahmed Kadry2, Ahmed Torad2, Mostafa Elwan2, Hugo Posada-Quintero1. 1University of Connecticut, Storrs, CT. 2Clarkson University, Potsdam, NY. 3Barry University, Miami Shores, FL. 4Wake Forest University, Winston-Salem, NC. 5State University of New York Plattsburgh, Plattsburgh, NY. BACKGROUND: People that want to “zone-out” may use noise cancellation headphones. Balance between primary and secondary stabilizing muscles ensures proper body posture free of musculoskeletal problems. Headphones use may be a neck stressor placing increased weight on cervical spinae muscles altering their stabilizing effect. The intermuscular coordination of the cervical erector spinae using headphones while walking is unexplored. METHODS: Participants (M = 13, F= 13, age = 21 ± 6 yrs) asked to walk (20-meter track for 30 minutes), while wearing noise cancellation headphones. Electromyographic (EMG) activity from right and left cervical erector (CER, CEL) was collected. For each muscle and at a 5-min interval, we obtained 10 time series of EMG band power. For each pair of EMG frequency bands between CER and CEL, cross-frequency interactions among EMG frequency bands were examined by bivariate equal-time Pearson’s cross-correlations. Hierarchical structure of the network’s links strength was dissected into separate network modules for low (F1, F2, F3), intermediate (F4, F5, F6, F7), and high (F8, F9, F10) EMG frequency bands, representing the activation of different muscle fiber types. RESULTS: The CER-CEL network showed a hierarchical organization with a clear stratification profile, with corresponding dominant links strength interactions to low-low ([F1-F2]—[F1-F2]), intermediate-high ([F3-F7]—[F8-F10]) and high-high ([F8-F10]—[F8-F10]) EMG frequency bands. While this hierarchical organization is preserved across the six 5-min intervals, the average link strength of the CER-CEL network is significantly reduced for all network modules (p \u3c 0.05). CONCLUSION: The CER-CEL network shows overexpressed/excessive inter-muscular connectivity at the beginning of the trial, reflecting reduced efficiency and lower degree of adaptability (i.e., rigidity) due to headphones’ use. As participants get adapted to the walking and the headphones, the CER-CEL network becomes sparser, indicating improved intermuscular network functionality. Neck musculature needs approximately 10 minutes to acclimate to the weight of the headphones. Clinicians working with people, who have vestibular issues, may place a weight in the form of headphones (or comparable apparatus/weight) while performing walking activities to facilitate neural activity of the cervical extensors through the process of vestibular adaptation and/or habituation