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The purpose was to determine whether the benefits of a muscle strength and power training program are superior to those achieved with muscle strength training alone in people with patellofemoral pain (PFP). Thirty-seven individuals with PFP (25.86 ± 7.62 years) were randomly assigned to a strength and power training group ( n  = 18) or strength training group ( n  = 19). Primary outcomes were pain intensity and physical functioning. Secondary outcomes were kinesiophobia, quality of life, maximal isometric torque and rate of torque development of the hip and knee and self-perception of recovery. Primary outcomes were measured at baseline, after six weeks, after 12 weeks and at three, six- and 12-months follow-ups. Secondary outcomes were measured at baseline and after 12 weeks. The significance level was 0.05. After interventions, no significant differences were found between groups for primary or secondary outcomes. No difference between groups were found for pain or physical functioning at the other assessment times. Results of this study indicated that addition of power exercises to strength training for hip and knee muscles did not result in superior benefits compared to strength training alone.

The integration of Large Language Models (LLMs) in medical education has gained significant attention, particularly in their ability to handle complex medical knowledge assessments. However, a comprehensive evaluation of their performance in anatomical education remains limited. To evaluate the performance accuracy of current LLMs compared to previous versions in answering anatomical multiple-choice questions and assessing their reliability across different anatomical topics. We analyzed the performance of four LLMs (GPT-4o, Claude, Copilot, and Gemini) on 325 USMLE-style MCQs covering seven anatomical topics. Each model attempted the questions three times. Results were compared with the previous year’s GPT-3.5 performance and random guessing. Statistical analysis included chi-square tests for performance differences. Current LLMs achieved an average accuracy of 76.8 ± 12.2%, significantly higher than GPT-3.5 (44.4 ± 8.5%) and random responses (19.4 ± 5.9%). GPT-4o demonstrated the highest accuracy (92.9 ± 2.5%), followed by Claude (76.7 ± 5.7%), Copilot (73.9 ± 11.9%), and Gemini (63.7 ± 6.5%). Performance varied significantly across anatomical topics, with Head & Neck (79.5%) and Abdomen (78.7%) showing the highest accuracy rates, while Upper Limb questions showed the lowest performance (72.9%). Only 29.5% of questions were answered correctly by all LLMs, and 2.5% were never answered correctly. Statistical analysis confirmed significant differences between models and across topics (χ 2 = 182.11–518.32, p  < 0.001). Current LLMs show markedly improved performance in anatomical knowledge assessment compared to previous versions, with GPT-4o demonstrating superior accuracy and consistency. However, performance variations across anatomical topics and between models suggest the need for careful consideration in educational applications. These tools show promise as supplementary resources in medical education while highlighting the continued necessity for human expertise.

Transfer learning using a pre-trained model with the ImageNet database is frequently used when obtaining large datasets in the medical imaging field is challenging. We tried to estimate the value of deep learning for facial US images by assessing the classification performance for facial US images through transfer learning using current representative deep learning models and analyzing the classification criteria. For this clinical study, we recruited 86 individuals from whom we acquired ultrasound images of nine facial regions. To classify these facial regions, 15 deep learning models were trained using augmented or non-augmented datasets and their performance was evaluated. The F-measure scores average of all models was about 93% regardless of augmentation in the dataset, and the best performing model was the classic model VGGs. The models regarded the contours of skin and bones, rather than muscles and blood vessels, as distinct features for distinguishing regions in the facial US images. The results of this study can be used as reference data for future deep learning research on facial US images and content development.

As early as 1955, there were studies on the multiple bellies of the temporalis muscle (TM), which is considered to contain two or three layers. However, some historical literature proposed that the sphenomandibularis (SM), one of the bellies of TM, should be considered a newly discovered independent muscle. Many scholars have extremely inconsistent views on this statement. Here, we conducted an anatomical study to clarify the existence and morphological characteristics of the SM. We dissected 14 formaldehyde-fixed and 6 fresh-frozen human cadaver heads using three approaches, including vascular casting in 10 cases. 15 sets of consecutive P45 sheet‑plastinated sections were analyzed, including 5 coronal, 5 sagittal, and 5 horizontal planes. SM has distinct anatomical features. This strip-like muscle extends from the maxillary surface of the greater wing of the sphenoid bone to the temporal crest behind the retromolar trigone of the mandible. Ours is the first detailed description of SM through combining gross anatomy with the P45 sheet plastination technique. The special anatomical location of this belly may be related to compression of the maxillary nerve, anatomy of the pterygopalatine fossa, identification of the buccal nerve, comprehension of the fascial-tendinous complex, and a deeper understanding of the masticatory system. We recommend the name Sphenomandibularis as a further reference and to distinguish it from TM.

We evaluated the addition of Hermetia illucens larvae (BSFL) fat to broiler diets compared to that of soybean oil (SO), rapeseed oil (RO), palm oil (PO), palm kernel fatty acid distillate (PKFD), poultry fat (PF), pig lard (PL), and beef tallow (BT) on performance, meat quality, and cecal microbiota abundance. BSFL addition reduced the feed conversion ratio compared to PO, PF, and BT addition and improved nutrient digestibility at 14 days and 28 days of age. BSFL addition enhanced broiler immune responses compared with SO, PO, and PL by increasing IgA levels; compared to PO by increasing IgG levels; and compared to PL by increasing IgY and IL-6 levels. Moreover, broiler free triiodothyronine levels increased when BSFL was added. Reduced drip loss after 7 days was observed in the RO, PO, PF, PL, and BT groups, while the salt-induced water uptake in the PL group was greater than that in the BSFL group. Compared with the SO group, BSFL addition increased C18:3 and overall n-3 FAs in breast meat. BSFL fat in broiler diets leads to outcomes that are more similar to those of plant oils than to those of animal fats, particularly regarding nutrient digestiblity and immune response.

Individuals with lower-limb amputations, many of whom have type 2 diabetes, experience impaired musculoskeletal health. This study: (1) compared residual and intact limbs of diabetic and non-diabetic post-mortem individuals with amputation to identify structures vulnerable to injury, and (2) compared findings to diabetic and healthy control groups to differentiate influences of amputation and diabetes on musculoskeletal health. Postmortem CT scans of three groups, ten individuals each, were included: (1) individuals with transtibial or transfemoral amputations, half with diabetes (2) diabetic controls, and (3) healthy controls. Hip and knee joint spaces, cross-sectional thigh muscle and fat areas, and cross-sectional bone properties (e.g. area, thickness, geometry) were measured. Wilcoxon Signed-Rank and Kruskal–Wallis tests assessed statistical significance. Asymmetry percentages between limbs assessed clinical significance. Residual limbs of individuals with amputation, particularly those with diabetes, had significantly less thigh muscle area and thinner distal femoral cortical bone compared to intact limbs. Compared to control groups, individuals with amputation had significantly narrower joint spaces, less thigh muscle area bilaterally, and thinner proximal femoral cortical bone in the residual limb. Diabetic individuals with amputation had the most clinically significant asymmetry. Findings tended to align with those of living individuals. However, lack of available medical information and small sample sizes reduced the anticipated clinical utility. Larger sample sizes of living individuals are needed to assess generalizability of findings. Quantifying musculoskeletal properties and differentiating influences of amputation and diabetes could eventually help direct rehabilitation techniques.

Despite the relative ease of locating organs in the human body, automated organ segmentation has been hindered by the scarcity of labeled training data. Due to the tedium of labeling organ boundaries, most datasets are limited to either a small number of cases or a single organ. Furthermore, many are restricted to specific imaging conditions unrepresentative of clinical practice. To address this need, we developed a diverse dataset of 140 CT scans containing six organ classes: liver, lungs, bladder, kidney, bones and brain. For the lungs and bones, we expedited annotation using unsupervised morphological segmentation algorithms, which were accelerated by 3D Fourier transforms. Demonstrating the utility of the data, we trained a deep neural network which requires only 4.3 s to simultaneously segment all the organs in a case. We also show how to efficiently augment the data to improve model generalization, providing a GPU library for doing so. We hope this dataset and code, available through TCIA, will be useful for training and evaluating organ segmentation models.Measurement(s)organ subunit • image segmentation • brain segmentation • anatomical phenotype annotationTechnology Type(s)unsupervised machine learning • Manual • computed tomography • supervised machine learningFactor Type(s)human organSample Characteristic - OrganismHomo sapiensMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13055663

The deep transverse perineal muscle was considered a striated muscle within the deep perineal space; however, recent studies suggest that it consists of smooth muscle. We investigated the histological composition and spatial distribution of the deep perineal space and its anatomical relationships with surrounding structures to understand its role in the perineum. Perineal muscle shape and location from nine cadavers (five males; average age, 79 years) were macroscopically examined. Serial sections were histologically analyzed using Masson’s trichrome, Elastica Masson, and immunostaining of smooth muscle tissue, from which the structure was three-dimensionally reconstructed. An overall layered perineum structure was revealed in both sexes. A slender, band-like striated muscle was observed near the surface. Beneath this layer was a dense connective tissue membrane that provides separation. A triangular, plate-like shaped smooth muscle with transversely oriented fibers occupied the deep perineal space, which, unlike the striated muscle, was continuous with the rectal wall and was identified as the deep transverse perineal muscle. The smooth muscle was continuous with the longitudinal rectal muscle and, in females, with the vaginal muscular wall. Findings indicate that the deep transverse perineal muscle may be a distinct pelvic smooth muscle component.

Binge drinking is the most common form of alcohol abuse and 25–40% of orthopedic trauma patients are intoxicated upon admission. While alcohol consumption prolongs the healing time of bone fractures, the underlying mechanisms by which alcohol leads to this fracture healing disorder remain unclear. 240 young adult and aged male C57BL/6J mice were examined. Half of the animals were acutely alcoholized two hours before surgery. The isolated fracture (Fx) group underwent an externally stabilized osteotomy and another group (THFx) an additional trauma hemorrhage (TH). The Sham group only received catheterization and fixation. Histological, radiological, biomechanical and systemic examinations were performed. Data were analyzed using two-way ANOVA and p  ≤ 0.05 was considered significant. After 3 weeks, acute alcoholization led to increased receptor activator of NF-κB ligand (RANKL), osteoprotegerin (OPG) and RANKL/OPG ratio after fracture in young adult mice. In aged animals, this effect of alcohol was not present as values were already increased in the sober ones. More osteoblasts and macrophages were found in alcoholized young Fx animals. In conclusion, one-time binge drinking before trauma causes a long-term change in the RANKL/OPG ratio towards more RANKL in young adult mice.

Microbial metabolites are known to modulate immune responses of the host. The main metabolites derived from microbial fermentation of dietary fibers in the intestine, short-chain fatty acids (SCFA), affect local and systemic immune functions. Here we show that SCFA are regulators of osteoclast metabolism and bone mass in vivo. Treatment of mice with SCFA as well as feeding with a high-fiber diet significantly increases bone mass and prevents postmenopausal and inflammation-induced bone loss. The protective effects of SCFA on bone mass are associated with inhibition of osteoclast differentiation and bone resorption in vitro and in vivo, while bone formation is not affected. Mechanistically, propionate (C3) and butyrate (C4) induce metabolic reprogramming of osteoclasts resulting in enhanced glycolysis at the expense of oxidative phosphorylation, thereby downregulating essential osteoclast genes such as TRAF6 and NFATc1. In summary, these data identify SCFA as potent regulators of osteoclast metabolism and bone homeostasis. Short-chain fatty acids (SCFA) are a main class of metabolites derived from fermentation of dietary fibre in the intestine. Here, the authors show that dietary administration of SCFA is associated with inhibition of osteoclast differentiation, increased bone mass, and reduced pathological bone loss in mice.