Search Results (733)

This study assessed the effects of different prepartum supplementation plans on Nellore cows’ performance, metabolic responses, and early offspring development. Thirty-nine pregnant Nellore cows (224 ± 2.67 days of pregnancy, 5.3 ± 0.29 years of age, body weight 520 ± 15.2 kg, initial body condition score 6.0 ± 0.07) were assigned to one of four treatments: a control group receiving only mineral mixture ad libitum, and three groups receiving daily protein-energy supplements of 2, 4, or 6 g/kg BW for 60 days prepartum. Weights and body condition scores were evaluated at the start of the experiment, 7 days before calving, and at 45 and 90 days postpartum. Cows supplemented with 4 and 6 g/kg BW showed improved body weight and body condition scores prepartum and postpartum and had a shorter service period (p < 0.05). The highest blood urea nitrogen concentrations were observed in cows receiving 6 g/kg BW (p = 0.0124). There was a reduction in blood urea nitrogen at calving for the 6 g/kg BW group, while the control group showed an increase (p < 0.001). Non-esterified fatty acids concentrations were lower 21 days before calving for the 4 and 6 g/kg BW groups compared to the control (p < 0.05) and decreased postpartum for all treatments (p < 0.001). No significant differences were observed in calf birth weight or performance. Supplementing with 4 g/kg BW of protein-energy is recommended to enhance metabolic health and overall performance. Full article

The purpose of this study was to examine the impact of listening to classical music on functional (upper and lower body strength, functional mobility and aerobic endurance) and cognitive (attentional capacities and working memory (WM)) performances in women aged between 50 and 60 years old. A total of 24 middle-aged women were enrolled to participate in this study. Their functional and cognitive performances were assessed under two-auditory conditions (no-music vs. with music conditions) using the Timed Up and Go (TUG) test for functional mobility, the Arm Curl test and 30 s Chair Stand Tests for the upper and lower body strength, respectively, and the 2 min Step test for aerobic endurance. To assess the attentional capacities and the WM, a simple reaction time (SRT) test and Corsi Block-Tapping Task were used, respectively. As a result, we found that listening to music significantly decreased the scores of the TUG test (p < 0.001) and capacities (p < 0.05), and increased the 2 min Step test values (p < 0.001) compared to the no-music condition. However, no significant changes were found for the upper and lower body strength and WM. We conclude that listening to classical music, i.e., Mozart’s Symphony, is effective in improving functional mobility, aerobic endurance and attentional capacities in middle-aged women. However, these gains were absent for muscle strength and WM, suggesting that the positive effects of music on functional and cognitive performances were dependent on a specific task. Full article

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual’s sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars—three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors. Full article

Nonalcoholic fatty liver disease (NAFLD), or metabolic dysfunction-associated steatotic liver disease (MASLD), is a liver condition that is linked to overweight, obesity, diabetes mellitus, and metabolic syndrome. Nonalcoholic steatohepatitis (NASH), or metabolic dysfunction-associated steatohepatitis (MASH), is a form of NAFLD/MASLD that progresses over time. While steatosis is a prominent histological characteristic and recognizable grossly and microscopically, liver biopsies of individuals with NASH/MASH may exhibit several other abnormalities, such as mononuclear inflammation in the portal and lobular regions, hepatocellular damage characterized by ballooning and programmed cell death (apoptosis), misfolded hepatocytic protein inclusions (Mallory–Denk bodies, MDBs), megamitochondria as hyaline inclusions, and fibrosis. Ballooning hepatocellular damage remains the defining feature of NASH/MASH. The fibrosis pattern is characterized by the initial expression of perisinusoidal fibrosis (“chicken wire”) and fibrosis surrounding the central veins. Children may have an alternative form of progressive NAFLD/MASLD characterized by steatosis, inflammation, and fibrosis, mainly in Rappaport zone 1 of the liver acinus. To identify, synthesize, and analyze the scientific knowledge produced regarding the implications of using a score for evaluating NAFLD/MASLD in a comprehensive narrative review. The search for articles was conducted between 1 January 2000 and 31 December 2023, on the PubMed/MEDLINE, Scopus, Web of Science, and Cochrane databases. This search was complemented by a gray search, including internet browsers (e.g., Google) and textbooks. The following research question guided the study: “What are the basic data on using a score for evaluating NAFLD/MASLD?” All stages of the selection process were carried out by the single author. Of the 1783 articles found, 75 were included in the sample for analysis, which was implemented with an additional 25 articles from references and gray literature. The studies analyzed indicated the beneficial effects of scoring liver biopsies. Although similarity between alcoholic steatohepatitis (ASH) and NASH/MASH occurs, some patterns of hepatocellular damage seen in alcoholic disease of the liver do not happen in NASH/MASH, including cholestatic featuring steatohepatitis, alcoholic foamy degeneration, and sclerosing predominant hyaline necrosis. Generally, neutrophilic-rich cellular infiltrates, prominent hyaline inclusions and MDBs, cholestasis, and obvious pericellular sinusoidal fibrosis should favor the diagnosis of alcohol-induced hepatocellular injury over NASH/MASH. Multiple grading and staging methods are available for implementation in investigations and clinical trials, each possessing merits and drawbacks. The systems primarily used are the Brunt, the NASH CRN (NASH Clinical Research Network), and the SAF (steatosis, activity, and fibrosis) systems. Clinical investigations have utilized several approaches to link laboratory and demographic observations with histology findings with optimal platforms for clinical trials of rapidly commercialized drugs. It is promising that machine learning procedures (artificial intelligence) may be critical for developing new platforms to evaluate the benefits of current and future drug formulations. Full article

Morphological scoring is a common evaluation method for domestic animals. The National Association of Maremmano Breeders (ANAM) has provided a dataset containing the records of 600 horses, four metric measurements (cm) and 24 traits with a continuous evaluation scale, each one with 15 classes. Moreover, a body condition score (BCS) with five classes is included. In this study, factor analysis was conducted to create a small number of informative factors (3) obtained from these traits, and a new BLUP-AM-MT index was established. The New Estimated Breeding Value (NEBV1) of each horse was computed by adding the genetic indexes of the three factors, with each one multiplied using a coefficient indicated by ANAM. The practical feasibility of the NEBV1 was evaluated through Spearman correlations between the rankings of the NEBV1 and the rankings of the BLUP-AM-MT, estimated through the four biometric measures and the morphological score (MS) assigned to each horse by the ANAM judges. The factorial analysis was used to estimate three factors: the “Trunk Dimension”, “Legs” and “Length”. As the explained variance was only 32%, the model was rotated, and the heritability of the three factors were 0.51, 0.05 and 0.41, respectively. After rotation, the estimated correlations between the new NEBV1 and the biometric measures were improved. These results should encourage breeders to adopt a breeding value index that takes into consideration the factors derived from all the variables observed in the morphological evaluation of the Maremmano. In this way, breeders can use it to select the best animals for breeding. Full article

Tremor, defined as an “involuntary, rhythmic, oscillatory movement of a body part”, is a key feature of many neurological conditions including Parkinson’s disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson’s disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81–0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools. Full article

Sleep apnea, characterized by breathing interruptions or slow breathing at night, can cause various health issues. Detecting respiratory rate (RR) using Wireless Fidelity (Wi-Fi) can identify sleep disorders without physical contact avoiding sleep disruption. However, traditional methods using Network Interface Cards (NICs) like the Intel Wi-Fi Link 5300 NIC are often costly and limited in channel state information (CSI) resolution. Our study introduces an effective strategy using the affordable ESP32 single-board computer for tracking RR through detailed analysis of Wi-Fi signal CSI. We developed a technique correlating Wi-Fi signal fluctuations with RR, employing signal processing methods—Hampel Filtering, Gaussian Filtering, Linear Interpolation, and Butterworth Low Pass Filtering—to accurately extract relevant signals. Additionally, noise from external movements is mitigated using a Z-Score for anomaly detection approach. We also implemented a local peak function to count peaks within an interval, scaling it to bpm for RR identification. RR measurements were conducted at different rates—Normal (12–16 bpm), Fast (>16 bpm), and Slow (<12 bpm)—to assess the effectiveness in both normal and sleep apnea conditions. Tested on data from 8 participants with distinct body types and genders, our approach demonstrated accuracy by comparing modeled sleep RR against actual RR measurements from the Vernier Respiration Monitor Belt. Optimal parameter settings yielded an overall average mean absolute deviation (MAD) of 2.60 bpm, providing the best result for normal breathing (MAD = 1.38). Different optimal settings were required for fast (MAD = 1.81) and slow breathing (MAD = 2.98). The results indicate that our method effectively detects RR using a low-cost approach under different parameter settings. Full article

Background/Objectives: Prediction of a difficult airway during pre-anesthetic evaluation is of great importance because it enables an adequate anesthetic approach and airway management. As there is a scarcity of prospective studies evaluating the role of anthropometric measures of the face and neck in predicting difficult airways in infants with an apparently normal airway, we aimed to identify the aforementioned predictors of difficult facemask ventilation and intubation in infants. Methods: A prospective, observational study that included 97 infants requiring general endotracheal anesthesia was conducted. Anthropometric and specific facial measurements were obtained before ventilation and intubation. Results: The incidence of difficult facemask ventilation was 15.5% and 38.1% for difficult intubation. SMD (sternomental distance), TMA (tragus-to-mouth angle distance), NL (neck length) and mouth opening were significantly lower in the difficult facemask ventilation group. HMDn (hyomental distance in neutral head position), HMDe (hyomental distance in neck extension), TMD (thyromental distance), SMD, mandibular development and mouth opening were significantly different in the intubation difficulty group compared to the non-difficult group. HMDn and HMDe showed significantly greater specificities for difficult intubation (83.8% and 76.7%, respectively), while higher sensitivities were observed in TMD, SMD and RHSMD (ratio of height to SMD) (89.2%, 75.7%, and 70.3%, respectively). Regarding difficult facemask ventilation, TMA showed greater sensitivity (86.7%) and SMD showed greater specificity (80%) compared to other anthropometric parameters. In a multivariate model, BMI (body mass index), COPUR (Colorado Pediatric Airway Score), BOV (best oropharyngeal view) and TMA were found to be independent predictors of difficult intubation, while BMI, ASA (The American Society Physical Status Classification System), CL (Cormack–Lehane Score), TMA and SMD predicted difficult facemask ventilation. Conclusions: Preoperative airway assessment is of great importance for ventilation and intubation. Patient’s overall condition and facial measurements can be used as predictors of difficult intubation and ventilation. Full article

Background: Lymphedema is a debilitating condition that significantly affects quality of life due to its chronic nature and visible symptoms. Lymphaticovenous anastomosis (LVA) has emerged as a promising surgical intervention, yet its effects on body image and spiritual health alongside physical symptoms have not been thoroughly examined. This study evaluates the efficacy of LVA in improving symptoms, quality of life (QOL), body image, and spiritual well-being in lymphedema patients. Methods: A prospective cohort study was conducted at Kaohsiung Chang Gung Memorial Hospital, Taiwan, involving 44 patients with lymphedema undergoing LVA surgery. Evaluations were made pre-surgery, one month post-surgery, and six months post-surgery using the 36-Item Short Form Health Survey (SF-36), Multidimensional Body–Self Relations Questionnaire-Appearance Scales (MBSRQ-AS), and a spiritual health scale. Statistical analysis was performed using one-way repeated measures ANOVA. Results: Significant improvements were observed in lymphedema symptoms and QOL measures at six months post-operation. SF-36 results showed enhanced scores in nearly all domains, particularly in physical functioning and role-physical. The appearance orientation scores from the MBSRQ-AS significantly increased, indicating improved perceptions in some dimensions of body image. Conclusions: LVA surgery significantly enhances physical and psychological outcomes in patients with lymphedema, with marked improvements in symptoms, QOL, and body image perceptions. The findings suggest that while LVA is effective in addressing the physical and psychological aspects of lymphedema, it does not impact spiritual dimensions. This underscores the need for holistic approaches in the management of lymphedema to address all facets of patient well-being. Full article

Encephalitozoon cuniculi is a microsporidian, domestic rabbits being the main host. The disease can be acute or subclinical, but treatment options are limited and usually with unrewarding results; therefore, diagnosis and prevention of encephalitozoonosis in rabbits are of the utmost importance. This study aims to obtain the first preliminary information of the prevalence of E. cuniculi in the north-western region of Romania. A total of 176 rabbits were clinically examined and 2 mL of blood was sampled from each. An enzyme-linked immunosorbent assay (ELISA) kit by Medicago (Medicago, Uppsala, Sweden) on the resulted blood serum was utilized. Statistical analysis of the results was conducted using the EpiInfo 2000 software (CDC, Atlanta, GA, USA). A total prevalence of 39.2% (69/176) was identified, with statistically significant differences in relation to the rabbits’ clinical status, age, season of sampling, breeding system, body condition score and county of origin; the different family farms tested also had a statistically significant difference. This study gives the first preliminary information on this pathogen distribution on Romania’s territory, but further studies need to be performed on larger regions to declare the prevalence in the country. Full article

The aim of this work was to develop a liver tissue function index during the transition period of dairy cows managed in low-tropic conditions. In two farms, twenty crossbred and synthetic native cows during the peripartum period were selected, and blood samples were taken on days −30 and −15 prepartum, the calving day, and 7, 20, 35, 50, 65, 80 and 105 days postpartum for serum metabolic tests. On each measurement day, body condition scores (BCS) and parameters on nitrogen metabolism (total protein—TP, albumin—ALB, globulin—GLOB, urea), adipose tissue metabolism (cholesterol—COL, non-esterified fatty acids—NEFA) and two transaminases (alanine aminotransferase—ALT and aspartate aminotransferase—AST) were evaluated. Data analysis included the Spearman correlation, principal components, multiple linear regression and cluster analysis. Results showed that regarding the days after calving and BCS, a liver tissue function index can be constructed using the TP, urea, COL, ALT and NEFA. The estimated index generated three groupings, both by days after calving and BCS. In the former, the index discriminated the metabolic behavior in the prepartum, parturition and postpartum periods, while in the latter, the index discriminated between extreme (2.25, 2.50 and 4.25), slightly low (2.75 and 3.0) and slightly high (3.25 to 4) conditions. The results allow us to conclude that it is feasible to construct mathematical function indexes for liver function to monitor metabolic changes during highly demanding productive phases in dairy cows under tropical conditions. Full article

The objective of this study was to evaluate the effects of breed, lactation number, and lameness on lying time, milk yield, milk urea nitrogen concentration (MUN), progesterone concentration (P₄), and the calving-to-conception interval (CCI) of lactating dairy cows in Central Texas. A total of 84 lactating dairy cows (Holsteins, Jerseys, and crossbreeds) from a commercial dairy farm in Central Texas were randomly selected and enrolled in this study from October 2023 to February 2024. Cows (60 ± 7 DIM) were enrolled in cohorts weekly for five weeks and were randomly fitted with an IceQube pedometer (IceRobotics, Edinburgh, UK) to track lying time. Lameness and body condition scores (BCS) were recorded, and blood samples were collected once a week. Parameters of reproductive performance included insemination rate, conception rate, pregnancy rate, and the CCI. Monthly dairy herd improvement association (DHIA) testing included milk yield and MUN concentrations. Breed and lactation number had a significant effect on milk yield, MUN concentration, lying time, BCS, and lameness (p < 0.001). Lactation number had a significant effect on P₄ concentrations (p < 0.001). There was a positive correlation between lameness and milk yield (p = 0.014) and a negative correlation between lameness and MUN concentrations (p = 0.038). Full article

The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in the context of maintaining good quality of life. The use of cameras and edge computing with sensors provides a way of monitoring subjects without interrupting their normal routines, and has the advantages of local data processing and improved privacy. This work describes the development an intelligent system that takes the RGB frames of a video as input to classify the occurrence of brushing teeth, washing hands, and fixing hair. No action activity is considered. The RGB frames are first processed by two Mediapipe algorithms to extract body keypoints related to the pose and hands, which represent the features to be classified. The optimal feature extractor results from the most complex Mediapipe pose estimator combined with the most complex hand keypoint regressor, which achieves the best performance even when operating at one frame per second. The final classifier is a Light Gradient Boosting Machine classifier that achieves more than 94% weighted F1-score under conditions of one frame per second and observation times of seven seconds or more. When the observation window is enlarged to ten seconds, the F1-scores for each class oscillate between 94.66% and 96.35%. Full article

The perception of work is closely linked to body reactions that facilitate task performance. Previous studies have shown that psychosocial work factors significantly impact employee health on both psychological and physical levels, though their cross-sectional designs limit causal interpretations. In this study, participants performed sitting and standing tasks under four different levels of mental workload. The NASA-Task Load Index (NASA-TLX) assessed mental workload perception across six dimensions, while Rapid Entire Body Assessment (REBA) and Rapid Upper Limb Assessment (RULA) scores evaluated body postures for standing and sitting tasks, respectively. This study examined the effects of alarms, distractions, and time constraints—common psychosocial factors in healthcare environments—on human behavior. We compared NASA-TLX scores with corresponding REBA/RULA scores to evaluate how perceived mental workload affects body postures. One-way ANOVA assessed the impact of experimental conditions on response variables, and Pearson correlation analyses explored the relationships between psychosocial factors and these variables. Results indicated that alarm conditions most negatively impacted mental workload perception and body postures. Temporal demand and effort scores were particularly affected by psychosocial factors in both tasks. Gender influenced physical demand and performance scores (higher in females) for the standing task but did not affect REBA and RULA scores. These findings suggest that organizational and psychosocial factors significantly influence healthcare workers’ behavior, health, and patient safety. Further research is needed to evaluate the specific effects of psychosocial factors on both physical and mental workload to understand the relationship between overall task workload and occupational disorders. Full article

This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders. Full article