Tag: statistics

Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2021 Aug 10. doi: 10.5507/bp.2021.043. Online ahead of print.

ABSTRACT

BACKGROUND: The aim of the study was to calculate the short-term and long-term outcomes of curative-intent surgery in distal cholangiocarcinoma (DCC) patients to identify potential prognostic factors.

PATIENTS AND METHODS: A retrospective cohort study of 32 consecutive DCC patients treated with pancreaticoduodenectomy between 2009 – 2017. The clinicopathological and histopathological data were evaluated for prognostic factors using the univariable Cox regression analysis. The Overall Survival (OS) was estimated using the Kaplan-Meier analysis.

RESULTS: The study comprised a total of 32 patients, with a mean age of 65.8 (± 9.0) years at the time of surgery. R0 resection was achieved in 25 (86.2%) patients, 19 (65.5%) patients received adjuvant oncological therapy. The OS rates at 1, 3 and 5 years were 62.5%, 37.5% and 21.9%, respectively. The 90-day mortality was 3/32 (9.4%) accounting for one-fourth of the first-year mortality rate. The median OS was 28.5 months. The only statistically significant prognostic factor was vascular resection, which was associated with worse OS in the univariable analysis (HR: 3.644; 95%-CI: 1.179-11.216, P=0.025). An age less than 65 years, ASA grade I/II, hospital stay of fewer than 15 days, R0 resection, lymph node ratio less than 0.2 and adjuvant oncological therapy tended to be associated with better OS but without statistically significant relevance.

CONCLUSION: The main factor directly influencing the survival of DCC patients is surgical complications. Surgical mortality comprises a significant group of patients, who die in the first year following pancreaticoduodenectomy. Vascular resection is the most important negative prognostic factor for long-term survival.

PMID:34467956 | DOI:10.5507/bp.2021.043

Oper Neurosurg (Hagerstown). 2021 Sep 1:opab310. doi: 10.1093/ons/opab310. Online ahead of print.

ABSTRACT

BACKGROUND: The spheno-orbital region (SOR) is a complex anatomic area that can be accessed with different surgical approaches.

OBJECTIVE: To quantitatively compare, in a preclinical setting, microsurgical transcranial approaches (MTAs), endoscopic endonasal transpterygoid approach (EEA), and endoscopic transorbital approaches (ETOAs) to the SOR.

METHODS: These approaches were performed in 5 specimens: EEA, ETOAs (superior eyelid and inferolateral), anterolateral MTAs (supraorbital, minipterional, pterional, pterional-transzygomatic, and frontotemporal-orbitozygomatic), and lateral MTAs (subtemporal and subtemporal transzygomatic). All specimens underwent high-resolution computed tomography; an optic neuronavigation system with dedicated software was used to quantify working volume and exposed area for each approach. Mixed linear models with random intercepts were used for statistical analyses.

RESULTS: Anterolateral MTAs offer a direct route to the greater wings (GWs) and lesser wings (LWs); only they guarantee exposure of the anterior clinoid. Lateral MTAs provide access to a large area corresponding to the GW, up to the superior orbital fissure (SOF) anteriorly and the foramen rotundum medially. ETOAs also access the GW, close to the lateral portion of SOF, but with a different angle of view as compared to lateral MTAs. Access to deep and medial structures, such as the lamina papyracea and the medial SOF, is offered only by EEA, which exposes the LW and GW only to a limited extent.

CONCLUSION: This is the first study that offers a quantitative comparison of the most used approaches to SOR. A detailed knowledge of their advantages and limitations is paramount to choose the ideal one, or their combination, in the clinical setting.

PMID:34467999 | DOI:10.1093/ons/opab310

Chemosphere. 2021 Jun 22;283:131309. doi: 10.1016/j.chemosphere.2021.131309. Online ahead of print.

ABSTRACT

Omics longitudinal studies are effective experimental designs to inform on the stability and dynamics of microbial communities in response to perturbations, but time-course analytical frameworks are required to fully exploit the temporal information acquired in this context. In this study we investigate the influence of ammonia on the stability of anaerobic digestion (AD) microbiome with a new statistical framework. Ammonia can severely reduce AD performance. Understanding how it affects microbial communities development and the degradation progress is a key operational issue to propose more stable processes. Thirty batch digesters were set-up with different levels of ammonia. Microbial community structure and metabolomic profiles were monitored with 16 S-metabarcoding and GCMS (gas-chromatography-mass-spectrometry). Digesters were first grouped according to similar degradation performances. Within each group, time profiles of OTUs and metabolites were modelled, then clustered into similar time trajectories, evidencing for example a syntrophic interaction between Syntrophomonas and Methanoculleus that was maintained up to 387 mg FAN/L. Metabolites resulting from organic matter fermentation, such as dehydroabietic or phytanic acid, decreased with increasing ammonia levels. Our analytical framework enabled to fully account for time variability and integrate this parameter in data analysis.

PMID:34467946 | DOI:10.1016/j.chemosphere.2021.131309

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