Body Parts Research Articles

The TALAPRO-3 study design: a plain language summary.

This summary is about the ongoing research study called TALAPRO-3. This study is testing the use of two medicines called talazoparib and enzalutamide. The two medicines are being used together as a treatment for patients with a type of cancer called metastatic castration-sensitive prostate cancer and changes in specific DNA repair genes within their tumors. The study began in May 2021, and includes 599 patients from 27 countries. Metastatic castration-sensitive prostate cancer is known as mCSPC for short. It is cancer that has started in the prostate and spread to other body parts. The prostate is a gland below the bladder and helps make semen (the liquid that contains sperm). Castration-sensitive means that the cancer responds to treatments that lower testosterone in the blood. In this study, some patients will take talazoparib plus enzalutamide while others will take a placebo plus enzalutamide. Talazoparib and enzalutamide are two different cancer medicines. Talazoparib is not currently used to treat patients with mCSPC. Enzalutamide is used to treat patients with prostate cancer. Talazoparib plus enzalutamide is being compared with a placebo plus enzalutamide to see if patients live longer without their cancer getting worse, or them dying, when taking talazoparib plus enzalutamide or when taking a placebo plus enzalutamide. This study aims to find out if treatment with talazoparib plus enzalutamide increases the length of time the patients in the study live without their cancer getting worse, or them dying, compared with treatment with a placebo plus enzalutamide. The study will also measure how long the patients in the study live, the number and types of side effects they have, their general health and quality of life, and whether there are changes in how patients report their pain.Clinical Trial Registration: NCT03395197 (TALAPRO-2) (ClinicalTrials.gov).

Functional Movement Disorder Is Associated with Abnormal Interoceptive Brain Activity: A Task-based Functional MRI Study.

Aberrant interoceptive processing has been hypothesized to contribute to the pathophysiology of functional neurological disorder, although findings have been inconsistent. Here, we utilized functional magnetic resonance imaging (fMRI) to examine neural correlates of interoceptive attention - the conscious focus and awareness of bodily sensations - in functional movement disorder (FMD). We used voxelwise analyses to compare blood oxygenation level-dependent responses between 13 adults with hyperkinetic FMD and 13 healthy controls (HCs) during a task requiring attention to different bodily sensations and to an exteroceptive stimulus. Additionally, we examined between-group differences in self-reported measures of interoception and evaluated their relationship with neural activity. Interoceptive conditions (heartbeat, stomach and 'body', indicating sensations from the body part or limb affected in FMD participants) activated a network involving the precuneus, the posterior cingulate cortex (PCC) and caudate nucleus (CN) bilaterally, and the right anterior insula (aINS) ( p <0.05 , corrected). Group differences in brain activity were mainly driven by processing of disease-related interoceptive signals, which in the FMD group was associated with a broader neural activation than monitoring gastric interoception, while no group differences were detected during cardiac interoception. Differences based on interoceptive focus ( body vs heartbeat and stomach) between FMD subjects and HCs were found in PCC, CN, angular gyrus, thalamus, and in the mid-insula ( p <0.05, corrected). This is, to our knowledge, the first study showing that FMD is associated with abnormal interoceptive processing in regions involved in monitoring body state, attentional focus, and homeostatic inference.

Theoretical proposal for restoration of hand motor function based on plasticity of motor-cortical interhemispheric interaction and its developmental rule.

After stroke, the poorer recovery of motor function of upper extremities compared to other body parts is a longstanding problem. Based on our recent functional MRI evidence on healthy volunteers, this perspective paper proposes systematic hand motor rehabilitation utilizing the plasticity of interhemispheric interaction between motor cortices and following its developmental rule. We first discuss the effectiveness of proprioceptive intervention on the paralyzed (immobile) hand synchronized with voluntary movement of the intact hand to induce muscle activity in the paretic hand. In healthy participants, we show that this bilateral proprioceptive-motor coupling intervention activates the bilateral motor cortices (= bilaterally active mode), facilitates interhemispheric motor-cortical functional connectivity, and augments muscle activity of the passively-moved hand. Next, we propose training both hands to perform different movements, which would be effective for stroke patients who becomes able to manage to move the paretic hand. This bilaterally different movement training may guide the motor cortices into left-right independent mode to improve interhemispheric inhibition and hand dexterity, because we have shown in healthy older adults that this training reactivates motor-cortical interhemispheric inhibition (= left-right independent mode) declined with age, and can improve hand dexterity. Transition of both motor cortices from the bilaterally active mode to the left-right independent mode is a developmental rule of hand motor function and a common feature of motor function recovery after stroke. Hence, incorporating the brain's inherent capacity for spontaneous recovery and adhering to developmental principles may be crucial considerations in designing effective rehabilitation strategies.

3D-printed microrobots for biomedical applications.

Microrobots, which can perform tasks in difficult-to-reach parts of the human body under their own or external power supply, are potential tools for biomedical applications, such as drug delivery, microsurgery, imaging and monitoring, tissue engineering, and sensors and actuators. Compared with traditional fabrication methods for microrobots, recent improvements in 3D printers enable them to print high-precision microrobots, breaking through the limitations of traditional micromanufacturing technologies that require high skills for operators and greatly shortening the design-to-production cycle. Here, this review first introduces typical 3D printing technologies used in microrobot manufacturing. Then, the structures of microrobots with different functions and application scenarios are discussed. Next, we summarize the materials (body materials, propulsion materials and intelligent materials) used in 3D microrobot manufacturing to complete body construction and realize biomedical applications (e.g., drug delivery, imaging and monitoring). Finally, the challenges and future prospects of 3D printed microrobots in biomedical applications are discussed in terms of materials, manufacturing and advancement.

Sexual harassment, abuse and intimate relationships between coaches and athletes: a systematic review

ABSTRACT While sexual harassment and abuse (SHA) can be perpetrated by various individuals in sport, coaches have been studied as one of the most frequent perpetrators due to their physical and emotional proximity to athletes. This systematic review attempted to evaluate the past 20 years of research (2003–2023) examining SHA and inappropriate intimate relationships between coaches and athletes. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline, 20 studies that met the eligibility criteria were identified using four databases (PubMed, PsycNET, ScienceDirect and SportDiscus). Findings reveal common thematic measures among the studies reviewed, including characteristics of SHA (n = 9), coach-athlete sexual relationships (CASR; n = 7) and grooming (n = 4). Based on the narrative synthesis of each thematic measure, SHA in sport is experienced and/or perceived as various forms of sexual misconduct, ranging from harassment (e.g. sexual jokes, unwanted comments, fondling) to abusive behaviors (e.g. kissing, showing coach's private body parts, sexual intercourse). Although some studies show the acceptance of CASR especially when athletes are above the age of legal consent, the strong majority of studies suggest that CASR in general is harmful and unacceptable given the unequal power dynamics between the two parties. Additionally, grooming was often involved as a conscious strategy to cross athletes’ personal boundaries for eventual sexual misconduct through building trust and manifesting seemingly innocuous behaviors. Overall, these findings shed light on the current state of research on SHA occurring between coaches and athletes and have implications for sport governing bodies and organizations to be better aware of the issue, which in turn can result in creating a safer environment for youth athletes.

A Boundary-Enhanced Decouple Fusion Segmentation Network for Diagnosis of Adenomatous Polyps.

Adenomatous polyps, a common premalignant lesion, are often classified into villous adenoma (VA) and tubular adenoma (TA). VA has a higher risk of malignancy, whereas TA typically grows slowly and has a lower likelihood of cancerous transformation. Accurate classification is essential for tailored treatment. In this study, we develop a deep learning-based approach for the localization and classification of adenomatous polyps using endoscopic images. Specifically, a pre-trained EGE-UNet is first adopted to extract regions of interest from original images. Multi-level feature maps are then extracted by the feature extraction pipeline (FEP). The deep-level features are fed into the Pyramid Pooling Module (PPM) to capture global contextual information, and the squeeze body edge (SBE) module is then used to decouple the body and edge parts of features, enabling separate analysis of their distinct characteristics. The Group Aggregation Bridge (GAB) and Boundary Enhancement Module (BEM) are then applied to enhance the body features and edge features, respectively, emphasizing their structural and morphological characteristics. By combining the features of the body and edge parts, the final output can be obtained. Experiments show the proposed method achieved promising results on two private datasets. For adenoma vs. non-adenoma classification, It achieved a mIoU of 91.41%, mPA of 96.33%, mHD of 11.63, and mASD of 2.33. For adenoma subclassification (non-adenomas vs. villous adenomas vs. tubular adenomas), it achieved a mIoU of 91.21%, mPA of 94.83%, mHD of 13.75, and mASD of 2.56. These results demonstrate the potential of our approach for precise adenomatous polyp classification.

Dynamic Metal-Coordinated Adhesive and Self-Healable Antifreezing Hydrogels for Strain Sensing, Flexible Supercapacitors, and EMI Shielding Applications.

Dynamic metal-coordinated adhesive and self-healable hydrogel materials have garnered significant attention in recent years due to their potential applications in various fields. These hydrogels can form reversible metal-ligand bonds, resulting in a network structure that can be easily broken and reformed, leading to self-healing capabilities. In addition, these hydrogels possess excellent mechanical strength and flexibility, making them suitable for strain-sensing applications. In this work, we have developed a mechanically robust, highly stretchable, self-healing, and adhesive hydrogel by incorporating Ca2+-dicarboxylate dynamic metal-ligand cross-links in combination with low density chemical cross-links into a poly(acrylamide-co-maleic acid) copolymer structure. Utilizing the reversible nature of the Ca2+-dicarboxylate bond, the hydrogel exhibited a tensile strength of up to ∼250 kPa and was able to stretch to 15-16 times its original length. The hydrogel exhibited a high fracture energy of ∼1500 J m-2, similar to that of cartilage. Furthermore, the hydrogel showed good recovery, fatigue resistance, and fast self-healing properties due to the reversible Ca2+-dicarboxylate cross-links. The presence of Ca2+ resulted in a highly conductive hydrogel, which was utilized to design a flexible resistive strain sensor. This hydrogel can strongly adhere to different substrates, making it advantageous for applications in flexible electronic devices. When adhered to human body parts, the hydrogel can efficiently detect limb movements. The hydrogel also exhibited excellent performance as a solid electrolyte for flexible supercapacitors, with a capacitance of ∼260 F/g at 0.5 A/g current density. Due to its antifreezing and antidehydration properties, this hydrogel retains its flexibility at subzero temperatures for an extended period. Additionally, the porous network and high water content of the hydrogel impart remarkable electromagnetic attenuation properties, with a value of ∼38 dB in the 14.5-20.5 GHz frequency range, which is higher than any other hydrogel without conducting fillers. Overall, the hydrogel reported in this study exhibits diverse applications as a strain sensor, solid electrolyte for flexible supercapacitors, and efficient material for electromagnetic attenuation. Its multifunctional properties make it a promising candidate for use in various fields as a state-of-the-art material.

A Novel Symmetric Fine-Coarse Neural Network for 3D Human Action Recognition Based on Point Cloud Sequences

Human action recognition has facilitated the development of artificial intelligence devices focusing on human activities and services. This technology has progressed by introducing 3D point clouds derived from depth cameras or radars. However, human behavior is intricate, and the involved point clouds are vast, disordered, and complicated, posing challenges to 3D action recognition. To solve these problems, we propose a Symmetric Fine-coarse Neural Network (SFCNet) that simultaneously analyzes human actions’ appearance and details. Firstly, the point cloud sequences are transformed and voxelized into structured 3D voxel sets. These sets are then augmented with an interval-frequency descriptor to generate 6D features capturing spatiotemporal dynamic information. By evaluating voxel space occupancy using thresholding, we can effectively identify the essential parts. After that, all the voxels with the 6D feature are directed to the global coarse stream, while the voxels within the key parts are routed to the local fine stream. These two streams extract global appearance features and critical body parts by utilizing symmetric PointNet++. Subsequently, attention feature fusion is employed to capture more discriminative motion patterns adaptively. Experiments conducted on public benchmark datasets NTU RGB+D 60 and NTU RGB+D 120 validate SFCNet’s effectiveness and superiority for 3D action recognition.

Flexible thin-film thermoelectric generators for human skin-heat harvesting: A numerical study

By harvesting thermal energy from human skin, self-powered wearable electronics have the potential to overcome the limitations of battery power, enabling long-term continuous operation. Flexible thermoelectric generators (TEGs) that can form a conformal contact with heat sources of any shape are critical for self-powered wearables. Nevertheless, practical implementations and efficient harvesting of heat energy from the human body are still hindered by factors that significantly diminish the power output of flexible TEGs. In this work, we present a design framework and computational platform optimized for modelling the harvesting of skin-heat energy using organic-based flexible thin-film TEGs. First, a three-dimensional numerical model based on the finite element method (FEM) has been developed, which enables the study and optimization of organic-based film thermoelectric (TE) device and TEG parameters, such as width, length, thickness, and interconnection dimensions, to maximize performance. Then, the optimized flexible TEG has been used to investigate the effect of human skin on wearable device applications in different parts of the body and environmental conditions. Mechanical interlayers designed to control various interface functions can alter junction resistances, leading to structures that are either highly conductive or insulating. Soft heat conductor (SHC) and soft heat insulator (SHI) layers are embedded to enhance the thermal interfaces and temperature difference across the TEG. As a result, embedding these soft heat layers can increase the temperature difference (ΔT) and open circuit voltage (VOC) along the TEG by ∼30 % over conventional flexible thin-film TEGs, while preserving mechanical softness and flexibility. Additionally, a study on the impact of the skin/TEG matching and heat flux at the skin/TEG interface is presented. Lastly, using a state-of-the-art post-treated poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) film-based TEG device model, we determined the maximum power output obtainable on various parts of the human body under three distinct environmental conditions (cold, room, and hot temperatures).

Information extraction from medical case reports using OpenAI InstructGPT

Background and objectiveResearchers commonly use automated solutions such as Natural Language Processing (NLP) systems to extract clinical information from large volumes of unstructured data. However, clinical text's poor semantic structure and domain-specific vocabulary can make it challenging to develop a one-size-fits-all solution. Large Language Models (LLMs), such as OpenAI's Generative Pre-Trained Transformer 3 (GPT-3), offer a promising solution for capturing and standardizing unstructured clinical information. This study evaluated the performance of InstructGPT, a family of models derived from LLM GPT-3, to extract relevant patient information from medical case reports and discussed the advantages and disadvantages of LLMs versus dedicated NLP methods. MethodsIn this paper, 208 articles related to case reports of foreign body injuries in children were identified by searching PubMed, Scopus, and Web of Science. A reviewer manually extracted information on sex, age, the object that caused the injury, and the injured body part for each patient to build a gold standard to compare the performance of InstructGPT. ResultsInstructGPT achieved high accuracy in classifying the sex, age, object and body part involved in the injury, with 94%, 82%, 94% and 89%, respectively. When excluding articles for which InstructGPT could not retrieve any information, the accuracy for determining the child's sex and age improved to 97%, and the accuracy for identifying the injured body part improved to 93%. InstructGPT was also able to extract information from non-English language articles. ConclusionsThe study highlights that LLMs have the potential to eliminate the necessity for task-specific training (zero-shot extraction), allowing the retrieval of clinical information from unstructured natural language text, particularly from published scientific literature like case reports, by directly utilizing the PDF file of the article without any pre-processing and without requiring any technical expertise in NLP or Machine Learning. The diverse nature of the corpus, which includes articles written in languages other than English, some of which contain a wide range of clinical details while others lack information, adds to the strength of the study.

Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients

PurposeTo evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patients, for the detection of acute appendicular fractures in the pediatric population on conventional X-ray radiography (CXR). Materials and methodsIn this retrospective study, anonymized extremities CXRs of pediatric patients (age <17 years), with or without fractures, were included. Six hundred CXRs (maintaining the positive-for-fracture and negative-for-fracture balance) were included, grouping them per body part (shoulder/clavicle, elbow/upper arm, hand/wrist, leg/knee, foot/ankle). Follow-up CXRs and/or second-level imaging were considered as reference standard. A deep learning algorithm interpreted CXRs for fracture detection on a per-patient, per-radiograph, and per-location level, and its diagnostic performance values were compared with the reference standard. AI diagnostic performance was computed by using cross-tables, and 95 % confidence intervals [CIs] were obtained by bootstrapping. ResultsThe final cohort included 312 male and 288 female with a mean age of 8.9±4.5 years. Three undred CXRs (50 %) were positive for fractures, according to the reference standard. For all fractures, the AI tool showed a per-patient 91.3% (95%CIs = 87.6–94.3) sensitivity, 76.7% (71.5-81.3) specificity, and 84% (82.1–86.0) accuracy. In the per-radiograph analysis the AI tool showed 85% (81.9–87.8) sensitivity, 88.5% (86.3–90.4) specificity, and 87.2% (85.7–89.6) accuracy. In the per-location analysis, the AI tool identified 606 bounding boxes: 472 (77.9 %) were correct, 110 (18.1 %) incorrect, and 24 (4.0 %) were not-overlapping. ConclusionThe AI algorithm provides good overall diagnostic performance for detecting appendicular fractures in pediatric patients.

Comparison of Measurement and Prognostic Power of SUV Between High-Definition and Standard PET Imaging in Non-Small Cell Lung Cancer Patients.

This study aimed to evaluate the measurement and prognostic ability of the SUVmax of whole-body tumors (SUVmaxwb) in non-small cell lung cancer (NSCLC) patients, comparing high-definition (HD) PET imaging with standard-definition (SD) PET imaging. Methods: The study included 242 consecutive NSCLC patients who underwent baseline 18F-FDG PET/CT from April 2018 to January 2021. Two imaging techniques were used: HD PET (using ordered-subsets expectation maximization with point-spread function modeling and time-of-flight techniques and smaller voxels) and SD PET (with ordered-subsets expectation maximization and time-of-flight techniques). SUVmaxwb was determined by measuring all the tumor lesions in the whole body, and tumor-to-background ratio (TBR) was calculated using the background SUVmean of various body parts. Results: The patient cohort had an average age of 68.3 y, with 59.1% being female. During a median follow-up of 29.6 mo, 83 deaths occurred. SUVmaxwb was significantly higher in HD PET than SD PET, with respective medians of 17.4 and 11.8. The TBR of 1,125 tumoral lesions was also higher in HD PET. Univariate Cox regression analysis showed that SUVmaxwb from both HD and SD PET were significantly associated with overall survival. However, after adjusting for TNM (tumor, node, metastasis) stage, only SUVmaxwb from SD PET remained significantly associated with survival. Conclusion: HD PET imaging in NSCLC patients yields higher SUVmaxwb and TBR, enhancing tumor visibility. Despite this, its prognostic value is less significant than SD PET after adjusting clinical TNM stage. Thus, consideration should be given to using HD PET reconstruction to increase tumor visibility, and SD PET is recommended for NSCLC patient prognostication and therapeutic evaluation, as well as for the classification of lung nodules.

Investigation of the reliability and validity of the Turkish version of the Comprehensive Coordination Scale in patients with Parkinson's disease

ObjectiveThe Comprehensive Coordination Scale (CCS) is a valid, reliable scale for evaluating motor coordination, described as the ability to produce context-dependent movements of multiple body parts in both spatial and temporal domains in patients with chronic stroke. The aim of this study was to develop a Turkish version of the CCS that can be used to evaluate coordination in Parkinson's disease patients and to test its validity and reliability. MethodsThirty-four patients with Parkinson’s disease (18 females, 16 males) with a mean age of 67.55± 10.93 years were included. The 9-Hole Peg Test (9 HPT), Timed Up and Go Test (TUG), Berg Balance Scale (BBS), Functional Reach Test (FRT), and Modified Hoehn and Yahr (H&Y) were administered to the patients. ResultsIn the correlation analysis for convergent validity, the CCS-TR showed a moderate negative correlation with both the 9 HPT and the TUG test (r=-0.684 and −0.641, respectively). There were also moderate positive correlations observed between the CCS-TR and the FRT and BBS (r=0.607 and 0.657, respectively). The total CCS-TR had excellent intrarater reliability (ICC=0.99; 95 % CI: 0.98–0.99) and interrater reliability (ICC=0.90; 95 % CI: 0.81–0.95). The CCS-TR subscales had excellent intrarater reliability (ICC: range 0.98 from 0.99). For intrarater agreement, the ICC was 0.99, and for interrater agreement, the ICC was 0.90 (excellent agreement). ConclusionThe results of the present study indicate that the Turkish version of the CCS is a reliable and valid coordination scale that can be used in patients with Parkinson’s disease.

An Adult Male with Multiple Skin Lesions

Leishmaniasis, caused by Leishmania spp. and transmitted by infected female phlebotomine sandflies, manifests in various forms. These include visceral, cutaneous, and post-kala-azar dermal leishmaniasis (PKDL). The World Health Organisation (WHO) has officially validated Bangladesh as the first country to eliminate visceral leishmaniasis. However, sustaining this achievement is challenging if PKDL cases remain unchecked because PKDL patients act as human reservoirs. Here, we depict a case of PKDL with mixed skin lesions involving the face and multiple body parts. We hope this will raise a concern among us to be vigilant for such cases and maintain the success of eliminating visceral leishmaniasis. J MEDICINE 2024; 25: 191-192

Body Parts from Bioprinting

3D bioprinting not only represents the future of&amp;nbsp;regenerative medicine, it can also facilitate more precise and more ethical scientific research.

스포츠 리듬운동을 이용한 기능성 인지보행훈련이 발달 장애 아동의 보행패턴과 인지능력에 미치는 영향

Objectives The purpose of this study was to help neurological disorders seen of the trends of children with developmental disabilities and improve their quality of life by investigating the functional cognitive gait in using the sports rhythm training on gait pattern and cognitive function in trends of children with developmental disabilities. Methods In order to achieve the purpose of this study, 39 trends of children with developmental disabilities at N located in K city, were selected at random and allocated 13 trends of children with developmental disabilities each for the Non Exercise group (NEG), Deficient Exercise group(DEG), Over Exercise group (OEG) and measured kinematic gait pattern and cognitive function before and after the experiment. The data obtained from this study were analyzed using the SPSS 26.0 statistical program the two-way repeated measures ANOVA and Bonferroni post-hoc test was performed. Results The conclusions obtained through this study were as follows. First, as a result of analyzing the change of gait pattern to functional cognitive gait in using the sports rhythm training program, there were significantly improved in velocity, cadence, step length, stride length in OEG compared with NEG and DEG after 24 weeks. Second, as a result of analyzing the change of cognitive function according to functional cognitive gait in using the sports rhythm training, there were significantly improved in body part, number display, movement, operation in OEG compared with NEG and DEG after 24 weeks. Conclusions As conclusions, this study confirmed that the functional cognitive gait in using the sports rhythm training program could improve the gait pattern and cognitive function in the trends of children with developmental disabilities.

A Positioning Alarm System for Explosive Impact Debris Protective Suit Based on an Accelerometer Array.

Protection suits are vital for firefighters' safety. Traditional protection suits physically protect firemen from burns, but cannot locate the position of bodily injuries caused by impact debris. Herein, we present a wearable impact debris positioning system for firefighter protection suits based on an accelerometer array. Wearable piezoelectric accelerometers are distributed regularly on the suit to detect the vibration on different body parts, which is conducive to determining the position of injured body parts. In addition, the injured parts can be displayed on a dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11 ms, attributed to the smart signal processing method. This work provides a reliable and smart method for locating and assessing the position of bodily injuries caused by impact debris, which is significant because it enables fire commanders to rescue injured firefighters in time.

Deep learning aided computer vision system for automated linear type trait evaluation in dairy cows

The assessment of traits is important in determining production potential, reproductive performance, and overall health of dairy cows. The assessment of these traits typically involves visual inspection and manual measurement, which can be time-consuming, subject to bias, and potentially distressing for the animals. To address these challenges, convolutional neural networks (CNNs)-aided non-invasive computer vision system was developed in the present study. This system consists of a depth camera to acquire the RGB images and depth information of cows. The DeepLabV3+ model, having the ResNet50 model as a backbone, was utilized to segment the body parts of cows from RGB images. Image processing-based algorithms were developed to extract key pixel locations for each trait from these segmented images. The system estimated trait dimensions utilizing 3D data of respective key points. The mean-IoU (intersection-over-union) values for the developed segmentation models were 93.46%, 91.25%, and 99.27% for side-view, back-view traits, and stature, respectively. Additionally, the vision system was able to estimate the trait dimensions with mean absolute percentage error (MAPE) below 6.0%. For a few traits, MAPE, however, exceeded 10.0%, indicating higher error. Inaccurate segmentation, imprecise key point extraction, visual overlaps of specific body parts, and variations in cow postures contribute to such errors. The developed system attained a Ratio of Performance to Deviation (RPD) above 1.2 for all traits, indicating its ability to estimate the dimensions of traits efficaciously. Thus, the present study demonstrated the potential of a CNN-based computer vision-based system for automating the trait measurement process in cows.

Finite element simulation of treatment with locking plate for distal fibula fractures.

An improved Finite Element Model(FEM) is applied to compare the biomechanical stability of plates with three different options in the treatment of distal fibula fractures in this study. The Computed Tomography(CT) scan of the knee to ankle segment of a volunteer was performed. A 3D fibula FEM was reconstructed based on the CT data. Three different loads (uni-pedal standing, torsion, and twisting) were applied, the same as in the experiments in the literature. The stresses and strains of the three options were compared under the same loads, using a 4-hole locking plate (Option A), a 5-hole locking plate (Option B), and a 6-hole locking plate (Option C) in a standard plate for lateral internal fixation. The simulation results show that all three options showed a stress masking effect. Option C had the best overall biomechanical performance and could effectively distribute the transferred weight. This is because option C has greater torsional stiffness and better biomechanical stability than options A and B, and therefore, option C is the recommended internal fixation method for distal fibula fractures. The Finite Element Analysis(FEA) method developed in this work applies to the stress analysis of fracture treatment options in other body parts.

Analisis Kritis Critical Book Review Buku Ajar Anatomi Fisiologi Karya Heni Puji Wahyuningsih dan Yuni Kusmiyati

Critical book review is one of the mandatory assignments given to students to review a book in the sense of focusing on evaluation (explanation, interpretation, and analysis) of the book's strengths and weaknesses and how the book affects the way of thinking and increases understanding of a field of study. Teaching materials for Physiological Anatomy by Heni Puji Wahyuningsih and Yuni Kusmiyati are a comprehensive learning resource to understand the structure and function of the human body. This book discusses various organ systems in detail, from the cellular to the macroscopic level. Its clear explanations and informative pictures make this book easy for readers to understand. This study aims to find out the discussion of teaching materials in detail. This study uses a literature study method, namely collecting data from various literature or scientific references and physiological anatomy books. The results of the critical analysis of the research are teaching materials discussing the physiological anatomy of the human body, where anatomy is the study of the structure and relationships between body parts of living things, and physiology is the study of the study of the function of body parts