Ultrasonographic description of tendons and ligaments at the palmar (plantar) aspect of the distal limb in the one humped camel (Camelus dromedarius).

OBJECTIVE: To provide complete anatomical and ultrasonographic description of tendons and ligaments at the palmar (plantar) aspect of the cannon and phalangeal regions of the one-humped camel. DESIGN: Forty-two (21 fore and 21 hind) clinically normal camel cadavers' limbs disarticulated at the carpal and tarsal joints and three clinically normal mature camels were included in the study. Six cadaver limbs (three fore and three hind) were dissected, and another six limbs specimens (three fore and three hind) were frozen at -20° for 1 week then sectioned transversely with an electric band saw at different distances distal to the carpometacarpal and tarsometatarsal joints. The ultrasonographic study was carried out on the live camels and 30 cadaveric limbs. The shape, echogenicity and measurements (thickness, width and cross-sectional area) of superficial digital flexor tendon (SDFT), deep digital flexor tendon (DDFT), suspensory ligament (SL), and sesamoidean ligaments were recorded and the differences in values between live animals and cadaveric specimens were statistically analysed. RESULTS: The shape and echogenicity of SDFT, DDFT, and SL, varied between proximal, middle, and distal thirds of the cannon bone and the phalangeal region. There was no significant difference between live animal and cadaveric specimens. CONCLUSION: This study provided complete description of tendons and ligaments at the palmar (plantar) aspect of the cannon and phalangeal region of the one humped camel. The data obtained serves as a reference guide for practicing veterinarians and for future studies on injury to ligaments and tendons of camel's distal extremity.

Predicting in-hospital mortality after transcatheter aortic valve replacement using administrative data and machine learning.

Transcatheter aortic valve replacement (TAVR) is the gold standard treatment for patients with symptomatic aortic stenosis. The utility of existing risk prediction tools for in-hospital mortality post-TAVR is limited due to two major factors: (a) the predictive accuracy of these tools is insufficient when only preoperative variables are incorporated, and (b) their efficacy is also compromised when solely postoperative variables are employed, subsequently constraining their application in preoperative decision support. This study examined whether statistical/machine learning models trained with solely preoperative information encoded in the administrative National Inpatient Sample database could accurately predict in-hospital outcomes (death/survival) post-TAVR. Fifteen popular binary classification methods were used to model in-hospital survival/death. These methods were evaluated using multiple classification metrics, including the area under the receiver operating characteristic curve (AUC). By analyzing 54,739 TAVRs, the top five classification models had an AUC ≥ 0.80 for two sampling scenarios: random, consistent with previous studies, and time-based, which assessed whether the models could be deployed without frequent retraining. Given the minimal practical differences in the predictive accuracies of the top five models, the L2 regularized logistic regression model is recommended as the best overall model since it is computationally efficient and easy to interpret.

Microbial inactivation kinetics of UV LEDs and effect of operating conditions: A methodological critical analysis.

Tiny ultraviolet (UV) light-emitting diodes (LED)s that are replacing the conventional energy-intensive mercury UV lamps have gained interest since the early 2000's because of their promising advantages. In the context of microbial inactivation (MI) of waterborne microbes, disinfection kinetics of those LEDs exhibited variations among studies, in terms of varying the UV wavelength, the exposure time, power, and dose (UV fluence) as well as other operational conditions. While reported results may appear contradictory when examined separately, they probably are not when analyzed collectively. As such, in this study, we carry out a quantitative collective regression analysis of the reported data to shed light on the kinetics of MI by the emerging UV LEDs technology alongside the effects of varying operational conditions. The main goal is to identify dose response requirements for UV LEDs and to compare them to traditional UV lamps in addition to ascertaining optimal settings that could help in achieving the optimal inactivation outcome for comparable UV doses. The analysis showed that kinetically, UV LEDs are as effective as conventional mercury lamps for water disinfection, and at times more effective, especially for UV resistant microbes. We defined the maximal efficiency at two wavelengths, 260-265 nm and 280 nm, among a wide range of available LED wavelengths. We also defined the UV fluence per log inactivation of tested microbes. At the operational level, we identified existing gaps and developed a framework for a comprehensive analysis program for future needs.

Correction: Abou-Elnour et al. Investigation of the Optical Properties for Quaternary Se60-xGe35Ga5Sbx (x = 0, 5, and 10) Chalcogenide Glass. Materials 2022, 15, 6403.

The authors would like to make a correction in a recently published paper [...].

The relationship between ratings of perceived exertion (RPE) and relative strength for a fatiguing dynamic upper extremity task: A consideration of multiple cycles and conditions.

The goal of this study was to evaluate the relationship between ratings of perceived exertion (RPE) and relative strength with respect to baseline for a fatiguing free dynamic task targeting the upper extremity, namely simulated order picking, and determine whether the relationship remains the same for different conditions (i.e., pace and weight) and with fatigue. Fourteen participants (seven males, seven females) performed four sessions that included two 45-min work periods separated by 15 min of rest. The work periods involved picking weighted bottles from shoulder height and packaging them at waist height for four combinations of bottle mass and picking rate: 2.5 kg-15 bottles per minute (bpm), 2.5 kg-10 bpm, 2.5 kg-5 bpm, and 1.5 kg-15 bpm. Participants reported their RPEs every 5 min and performed a maximum isometric shoulder flexion exertion every 9 min. Pearson product-moment correlation was used to evaluate the linear relationship between RPE and relative strength for each subject and work period. Then, the effects of condition and work period on the average relationship were assessed using a repeated-measures analysis of variance (ANOVA). For the first 45-min period, there were no significantly different correlations between RPE and relative strength across conditions (average r = -0.62 (standard deviation = 0.38); p = 0.57). There was a significant decrease in average correlation for the second work period (r = -0.39 (0.53)). These results suggest that individual subjective responses consistently increase while relative strength declines when starting from a non-fatigued state. However, correlations are weaker when re-engaging in work following incomplete recovery. Thus, starting fatigue levels should be accounted for when considering the expected relationship between RPE and relative strength.

Correction: Evaluation of the Level of Dural Sac Tip in Saudi Population: A Magnetic Resonance Imaging Study.

[This corrects the article DOI: 10.7759/cureus.32533.].

Explaining Predictive Model Performance: An Experimental Study of Data Preparation and Model Choice.

Although confirmatory modeling has dominated much of applied research in medical, business, and behavioral sciences, modeling large data sets with the goal of accurate prediction has become more widely accepted. The current practice for fitting predictive models is guided by heuristic-based modeling frameworks that lead researchers to make a series of often isolated decisions regarding data preparation and cleaning that may result in substandard predictive performance. In this article, we use an experimental design to evaluate the impact of six factors related to data preparation and model selection (techniques for numerical imputation, categorical imputation, encoding, subsampling for unbalanced data, feature selection, and machine learning algorithm) and their interactions on the predictive accuracy of models applied to a large, publicly available heart transplantation database. Our factorial experiment includes 10,800 models evaluated on 5 independent test partitions of the data. Results confirm that some decisions made early in the modeling process interact with later decisions to affect predictive performance; therefore, the current practice of making these decisions independently can negatively affect predictive outcomes. A key result of this case study is to highlight the need for improved rigor in applied predictive research. By using the scientific method to inform predictive modeling, we can work toward a framework for applied predictive modeling and a standard for reproducibility in predictive research.

An integrated socio-economic agent-based modeling framework towards assessing farmers' decision making under water scarcity and varying utility functions.

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers' decisions concerning their future farming practices when faced with potential water scarcity induced by future climate change. The proposed framework forecasts farmers' behavior assuming varying utility functions. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline. The model results indicated that modelling farmers as agents, who were solely interested in optimizing their agro-business budget, was only able to reproduce 35% of the answers provided by the farmers through a administered field questionnaire. Model simulations highlighted the importance of representing the farmers' combined socio-economic attributes when assessing their future decisions on land tenure. This approach accounts for social factors, such as the farmers' attitudes, subjective norms, social influence, memories of previous civil unrest and farming traditions, in addition to their economic utility to model farmer decision making. Under this scenario, correspondence between model simulations and farmers' answers reached 95%. Additionally, the model results show that when faced with the negative impacts of climate change, the majority of farmers seek adaptive measures, such as changing their crops and/or seeking new water sources, only when future water shortages were predicted to be low to moderate. Most opt to cease farming and allow their lands to urbanize or go fallow, when future water shortages were predicted to be high. Meanwhile, incorporating and modeling the social influence structures within the ABM diminished farmers' willingness to adapt and doubled their propensity to sell or quit their land. The proposed framework is able to account for a variety of utility functions and to successfully capture the actions and interactions between farmers and their environment; thus, it represents an innovative modeling approach for assessing farmers' behavior and decision-making in the face of future climate change. The nonspecific structure of the framework allows its application at any agriculturally dominated setting facing future water shortages promulgated by a changing climate.

Functional ANOVA for Upper Extremity Fatigue Analysis during Dynamic Order Picking.

OCCUPATIONAL APPLICATIONSMusculoskeletal disorders are prevalent among warehouse workers who engage in repetitive and dynamic tasks. To prevent such injuries, it is vital to identify the factors that influence fatigue in the upper extremities during these repetitive activities. Our study reveals that task factors, namely the bottle mass and picking rate, significantly influence upper extremity fatigue. In most cases, the fatigue indicator is a functional variable, meaning that the fatigue score or measurement is a curve captured over time, which could be modeled as a function. In this study, we demonstrate that functional data analysis tools, such as functional analysis of variance (FANOVA), prove more effective than traditional methods in specifying how task factors contribute to the development of fatigue in the upper extremities. Furthermore, since there are inherent differences among workers that could affect their fatigue development process, the data heterogeneity could be tackled by employing clustering methods.

Background: Preventing musculoskeletal disorders is a paramount safety concern for industries, with order pickers in warehouses being particularly vulnerable due to their repetitive and dynamic tasks. Understanding the factors contributing to upper-extremity fatigue in such settings is crucial. Purpose: This paper investigates the impact of task-related factors on two upper-extremity fatigue indicators: ratings of perceived fatigue and relative muscle strength. Several statistical approaches were used and compared in terms of their capability in eliciting these effects. Methods: Simulated over-shoulder, order-picking lab experiments were conducted under different combinations of two bottle loads and three picking paces. Fourteen participants, evenly distributed between genders, completed the experiment. A FANOVA was executed as the principal analytical approach, considering the functional nature of the two fatigue indicators measured over the work period. To underscore the benefits of considering the whole functional curve instead of discrete variables, we also conducted repeated-measures and two-way ANOVA as benchmark analyses. Results: FANOVA outcomes affirmed that both task factors (load and pace) significantly influenced both fatigue indicators. The FANOVA method identified larger effect sizes (0.11< ηp2 < 0.19) for both task factors compared to the conventional methods (0< ηp2 < 0.11), supporting the efficacy of FANOVA in identifying the importance of these factors. Conclusions: The FANOVA approach proved effective in detecting the impact of task factors on fatigue indicators, yielding superior results compared to conventional benchmark methods. To address participant heterogeneity, functional clustering and gender-based clustering were introduced into the FANOVA framework, both effectively mitigating this challenge. Notably, FANOVA with functional clusters had superior performance compared to the one with gender clustering, suggesting functional clustering as a more suitable method in overcoming participant heterogeneity.

Evaluation of the Level of Dural Sac Tip in Saudi Population: A Magnetic Resonance Imaging Study.

Background For the success of procedures such as caudal block, craniospinal irradiation (CSI), and management of lower back pain and to minimize the risk of dural puncture the exact level of dural sac (DS) termination should be known. Objective The evaluation of DS tip location in the Saudi population and exploring possible significant factors that could be used as predictors in clinical prognosis. Methods A total of 200 patients' lumbar sagittal Weighted T2 Magnetic Resonance Imaging (MRI) study were randomly selected from a single-center hospital in-between 2020 and 2021. The DS tip location was determined by generating a perpendicular line from the longitudinal axis of its termination to the corresponding level. Then naming it after an intervertebral disk or a corresponding vertebrate that is divided into three thirds (upper, middle, and lower). Results In most cases, the level of DS termination is at the middle part of S2 (26.5%), followed by the upper part of S2 (25.1%), and the lower part of S2 (20%). In Saudi nationals, the DS tip was in the middle S2 level at 21.5%, upper S2 level at 19.1%, and lower S2 level at 17%. Factors such as age, sex, cause of referral, and nationality had no statistical significance in relation to DS tip location. Conclusion The DS termination level in the Saudi population ranges from disk between L5-S1 to the lower third of S3. Moreover, nationality, age, and cause of referral were not significant in determining the DS termination level. Therefore, it is still important to individualize patients' treatment by using MRI for each case that requires it.

Investigation of the Optical Properties for Quaternary Se60-xGe35Ga5Sbx (x = 0, 5, and 10) Chalcogenide Glass.

A quenching technique was used to prepare the chalcogenide system of the Se60−xGe35Ga5Sbx (x = 0, 5, and 10 at. %), which was deposited as thin films onto glass substrates using a thermal evaporation technique. X-ray diffraction patterns were used for structure examination of the fabricated compositions, which exposes the amorphous nature of the deposited samples. Meanwhile, the chemical compositions of the prepared samples were evaluated and calculated via the energy-dispersive X-ray spectroscopy (EDX), which was in agreement with the measured compositional element percentages of the prepared samples. Based on the optical reflectance R and transmittance T spectra from the recorded spectrophotometric data ranging from 350 to 2500 nm, the influence of the Sb element on the Se60−xGe35Ga5Sbx thin films' optical properties was studied. The film thickness and the refractive index were calculated via Swanepoel's technique from optical transmittance data. It has been observed that the films' refractive index increases with increasing x value over the spectral range. The refractive index data were used to evaluate the dielectric constants and estimate dispersion parameters Eo and Ed using the Wemple−DiDomenico model. The optical energy gap Egopt was calculated for the tested compositions. The result of the optical absorption analysis shows the presence of allowed direct and indirect transitions.

Airborne bacterial and PM characterization in intensive care units: correlations with physical control parameters.

In this study, the spatial variation of airborne bacteria in intensive care units (ICUs) was characterized. Fine particulate matter and several physical parameters were also monitored including temperature and relative humidity. The results showed that the total bacterial load ranged between 20.4 and 134.3 CFU/m3 across the ICUs. Bacterial cultures of the collected samples did not isolate any multi-drug-resistant Gram-negative bacilli indicating the absence of such aerosolized pathogens in the ICUs. Meanwhile, particulate matter levels in several ICUs were found to exceed the international guidelines set for 24-h PM exposure. Moreover, examining bacterial load contribution by size suggested that bacteria with sizes less than 0.65 µm contributed the least to the total bacterial loads, while those with sizes between 0.65 and 1.1 µm contributed the most. A multiple linear regression model was also built to predict the bacterial loads in the ICUs. The regression analysis explained 77% of the variability observed in the measured bacterial concentrations. The model showed that the level of activity in the ICU rooms as well as its occupancy level had strong positive correlations with bacterial loads, while distance away from the patient had a non-linear relationship with measured loads. No statistically significant correlation was found between bacterial load and particulate matter concentrations.

Assessment of safety and quality aspects of boiling treatment of quail eggs.

A total of 300 quail eggs were collected randomly from different markets in Cairo and Giza Governorates. Five eggs were represented as one egg sample. Shell and content of each egg were examined for their microbiological contents, sensory evaluation and study of Escherichia coli O157 survival in artificially contaminated eggs. Moreover, qualitative detection of antimicrobial residues by seven plates microbiologically bioassay and confirmed by validated high-performance liquid chromatography (HPLC) methods for positively reacted antimicrobials in raw and boiled samples. There was a significant difference (P < 0·05) between the grading score of eggs after the boiling at 2-, 4-, 5- and 7-min. Based on the survival results, the refrigeration storage and boiling for 5 min of quail eggs was confirmed that such eggs are without E. coli O157. After the boil, the concentrations of oxytetracycline (OTC) and 4-Epi-OTC residues were significantly reduced, and there was no effect on the concentration of sulphadimidine (SDD), amoxicillin (AMO) and Diketo residues. Samples that exceeded the maximum residual limits (MRLs) were 17·0%, 12·0%, 10·0%, 16·0% and 14·0% for SDD, OTC, 4-Epi-OTC, AMO and Diketo, respectively. After boiling, no significant change was noted for SDD, AMO and Diketo, but all OTC and 4-Epi-OTC were completely below MRLs. Therefore, SDD and AMO with their metabolite (Diketo) are heat-stable antimicrobial residues with multiple human health hazards.

Explaining the Varying Patterns of COVID-19 Deaths Across the United States: 2-Stage Time Series Clustering Framework.

BACKGROUND: Socially vulnerable communities are at increased risk for adverse health outcomes during a pandemic. Although this association has been established for H1N1, Middle East respiratory syndrome (MERS), and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited. OBJECTIVE: Our 3 objectives are to determine how many distinct clusters of time series there are for COVID-19 deaths in 3108 contiguous counties in the United States, how the clusters are geographically distributed, and what factors influence the probability of cluster membership. METHODS: We proposed a 2-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we used time-series clustering to identify clusters with similar outcome patterns for the 3108 contiguous US counties. Multinomial logistic regression was used to explain the relationship between community-level predictors and cluster assignment. We analyzed county-level confirmed COVID-19 deaths from Sunday, March 1, 2020, to Saturday, February 27, 2021. RESULTS: Four distinct patterns of deaths were observed across the contiguous US counties. The multinomial regression model correctly classified 1904 (61.25%) of the counties' outbreak patterns/clusters. CONCLUSIONS: Our results provide evidence that county-level patterns of COVID-19 deaths are different and can be explained in part by social and political predictors.

A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor.

Existing ergonomic risk assessment tools require monitoring of multiple risk factors. To eliminate the direct observation, we investigated the effectiveness of an end-to-end framework that works with the data from a single wearable sensor. The framework is used to identify the performed task as the major contextual risk factor, and then estimate the task duration and number of repetitions as two main indicators of task intensity. For evaluation of the framework, we recruited 37 participants to complete 10 simulated work tasks in a laboratory setting. In testing, we achieved an average accuracy of 92% for task identification, 7.3% error in estimation of task duration, and 7.1% error for counting the number of task repetitions. Moreover, we showed the utility of the framework outputs in two ergonomic tools to estimate the risk of injury. Overall, we indicated the feasibility of using data from wearable sensors to automate the ergonomic risk assessment in workplaces.

Splenogonadal fusion: aiding detection and avoiding radical orchidectomy.

Splenogonadal fusion is a rare benign congenital anomaly in which there is an abnormal connection between the gonad and the spleen. It was first described over 100 years ago with limited reports in the literature since then. Its similarity in presentation to testicular neoplasia poses a significant challenge in diagnosis and management, often resulting in radical orchidectomy. We present the case of a 31-year-old man who presented with a rapidly growing left-sided testicular mass and suspicious ultrasound findings; histology from the subsequent radical inguinal orchidectomy showed findings consistent with splenogonadal fusion. We describe points for consideration in the clinical history, examination and imaging that could suggest splenogonadal fusion, including preoperative technetium-99m-sulfur colloid imaging and intraoperative frozen section evaluation, which may confirm the diagnosis and prevent unnecessary orchidectomy.

Splenogonadal fusion: aiding detection and avoiding radical orchidectomy.

Splenogonadal fusion is a rare benign congenital anomaly in which there is an abnormal connection between the gonad and the spleen. It was first described over 100 years ago with limited reports in the literature since then. Its similarity in presentation to testicular neoplasia poses a significant challenge in diagnosis and management, often resulting in radical orchidectomy. We present the case of a 31-year-old man who presented with a rapidly growing left-sided testicular mass and suspicious ultrasound findings; histology from the subsequent radical inguinal orchidectomy showed findings consistent with splenogonadal fusion. We describe points for consideration in the clinical history, examination and imaging that could suggest splenogonadal fusion, including preoperative technetium-99m-sulfur colloid imaging and intraoperative frozen section evaluation, which may confirm the diagnosis and prevent unnecessary orchidectomy.

Modeling the differences in the time-series profiles of new COVID-19 daily confirmed cases in 3,108 contiguous U.S. counties: A retrospective analysis.

OBJECTIVE: The COVID-19 pandemic in the U.S. has exhibited a distinct multiwave pattern beginning in March 2020. Paradoxically, most counties do not exhibit this same multiwave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases? (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? MATERIALS AND METHODS: We analyzed data from counties in the U.S. from March 1, 2020 to January 2, 2021. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated with the outbreak patterns. RESULTS: Three patterns were identified from the cluster solution including counties in which cases are still increasing, those that peaked in the late fall, and those with low case counts to date. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. DISCUSSION: The pattern of the outbreak is related both to the geographic location within the U.S. and several variables including population density and government response. CONCLUSION: The reported pattern of cases in the U.S. is observed through aggregation of the daily confirmed COVID-19 cases, suggesting that local trends may be more informative. The pattern of the outbreak varies by county, and is associated with important demographic, socioeconomic, political and geographic factors.