Academic publishing is at a critical juncture. The challenges faced by the academics are mired in controversy. Among theseare three hotly debated concerns. First is the issue of whether technological innovations such as artificial intelligence (AI)improves research efficiency or if its use sacrifices research integrity.Another is the controversy between paywall publishingand open access. Lastly, adapting an appropriate business model for sustainability is a contentious issue and the choice betweena commercial or a university-based publishing platform is a difficult one.

Traditional models of scientific investigation relied on tedious intellectual calisthenics in all aspects of research —identifying research gaps, reviewing of published literature, devising valid methodology, collecting data, analysing results, and,finally, drawing conclusions. With the advent of powerful tools employing artificial intelligence, these heavy tasks are efficientlycarried out. The dilemma lies in determining which parts of the work can be attributed to the authors and which are ascribedto the output of large language models (LLMs) and other automated assistance employed.Despite requiring adequate vettingby experts of these AI-aided output, many in the scientific community still question these methods. Can research employingAI be considered honest work? Will full disclosure answer doubts as to the integrity of the scientific work?

Indeed, LLMs just gather information that is already out there, albeit more efficiently. After all, science progresses bystanding on the shoulder of giants. AI makes such work comprehensive and efficient. Standing on those proverbial shoulders,however, require access to prior work, hence our next challenge in academic publishing--open access versus paid access.Paywalls limit the benefits of valuable research to institutions and universities with the capacity to pay. Excluded from these arethose from low resourced countries, with nations from the global south being affected disproportionately. Additionally, whilenumerous authors appreciate the features of open access as it improves their impact and visibility, many feel unduly burdenedsince the cost of publishing in this format is passed on to them.

This brings us to our third issue: who bears the cost of academic publishing? Indeed, it is a lucrative industry, generatingan annual revenue of US$19 billion and an estimated 40 percent profit margin. Many, however, find fault in this businessmodel as concerns about the profit motives of the commercial publishers far overshadow their sustainability goals.

How do we navigate this landscape of controversies? We, at the ACTA, as part of the community of scholars, would needto clarify our mission. Our goals for this publication should be consistent with our values. These values, such as scientific rigor,integrity, and accountability, should be reflected in our policies. We should be cognizant of the role we play in national scientificdiscourse while we endeavor to make an impact in the global scene. We are accountable to our stakeholders — nurturingearly career scholars, supplying evidence to health policymakers, and being accountable to those who provide resources tosustain us. This stewardship is essential so that ACTA will stand shoulder to shoulder with the giants on which science buildsupon to benefit future generations.

Artificial Intelligence ; Commerce ; Costs And Cost Analysis ; Disclosure ; Drawing ; Efficiency ; Family Characteristics ; Forecasting ; Goals ; Gymnastics ; Health ; Health Resources ; Industry ; Intelligence ; Inventions ; Language ; Literature ; Methods ; Play And Playthings ; Policy ; Publications ; Publishing ; Research ; Residence Characteristics ; Role ; Science ; Shoulder ; Social Responsibility ; Universities ; Ursidae ; Volition ; Work ; World Health Organization

Bullous hemorrhagic dermatosis (BHD) is a rare cutaneous manifestation characterized by tense hemorrhagic bullae that appear at sites distant from low molecular weight heparin (LMWH) injections, typically within seven days of exposure. As of March 2022, only 94 cases have been reported. It most commonly affects elderly males with predisposing factors for thromboembolism, such as carcinoma, and usually involves the extremities.

This case highlights the importance of maintaining a high index of suspicion for bullous hemorrhagic dermatosis (BHD) in patients receiving low molecular weight heparin, even beyond the typical 7-day window and in demographics not commonly affected. Early recognition and prompt discontinuation of the offending agent, as demonstrated in this atypical presentation involving a Filipino elderly woman with multiple comorbidities and no malignancy, can lead to favorable outcomes. Clinicians should be aware of this rare but reversible complication to avoid misdiagnosis and ensure appropriate management.

Human ; Female ; Aged: 65-79 Yrs Old ; Affect ; Aged ; Blister ; Carcinoma ; Causality ; Demography ; Diagnostic Errors ; Enoxaparin ; Extremities ; Heparin ; Heparin, Low-molecular-weight ; Index ; Injections ; Lead ; Male ; Molecular Weight ; Neoplasms ; Patients ; Research Report ; Skin Diseases ; Thromboembolism ; Women

This study predicts the potential mechanism of Hippocampus in the treatment of knee osteoarthritis(KOA) through network pharmacology, with preliminary verification using molecular docking and animal experiments. The database was used to screen the active chemical components of Hippocampus and the targets of KOA, and Gene Ontology(GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis, and molecular docking were performed on the relevant core targets to preliminarily explore the potential targets and mechanisms of Hippocampus in the treatment of KOA. A rat KOA model was constructed by intra-articular injection of sodium iodoacetate, and the rats were intervened with different doses of Hippocampus decoction and celecoxib. The expression of relevant targets was detected through hematoxylin-eosin(HE) staining, enzyme-linked immunosorbent assay(ELISA), RT-qPCR, and Western blot to further validate the network pharmacology results. A total of 23 drug-like components of the Hippocampus were screened, and 128 common targets with KOA were identified, involving interleukin-17(IL-17) signaling pathway, transcription factor(FoxO) signaling pathway, tumor necrosis factor(TNF) signaling pathway. Molecular docking results showed that the screened core chemical components exhibited good affinity with key targets. HE staining demonstrated that Hippocampus improved the morphology of the cartilage layer. ELISA confirmed that Hippocampus significantly reduced the levels of IL-6 and TNF-α in the serum of KOA rats. Western blot and RT-qPCR analysis showed that Hippocampus significantly reduced the expression of IL-6, TNF-α, matrix metalloproteinase(MMP) 13, IL-17A, nuclear factor κB activator 1(ACT1), tumor necrosis factor receptor-associated factor 6(TRAF6) and nuclear factor κB(NF-κB) in cartilage tissue. The results suggest that Hippocampus can alleviate the degree of joint damage in the KOA rat model induced by sodium iodoacetate. The mechanism of action is related to the inhibition of the IL-17 signaling pathway, reduction of inflammation, and inhibition of extracellular matrix(ECM) degradation.
Animals ; Molecular Docking Simulation ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Network Pharmacology ; Male ; Osteoarthritis, Knee/metabolism* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Humans ; Interleukin-17/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Hippocampus/chemistry*

In order to realize the quantitative assessment of muscle strength in hand function rehabilitation and then formulate scientific and effective rehabilitation training strategies, this paper constructs a multi-scale convolutional neural network (MSCNN) - convolutional block attention module (CBAM) - bidirectional long short-term memory network (BiLSTM) muscle strength prediction model to fully explore the spatial and temporal features of the data and simultaneously suppress useless features, and finally achieve the improvement of the accuracy of the muscle strength prediction model. To verify the effectiveness of the model proposed in this paper, the model in this paper is compared with traditional models such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN), CNN - squeeze excitation network (SENet), MSCNN-CBAM and MSCNN-BiLSTM, and the effect of muscle strength prediction by each model is investigated when the hand force application changes from 40% of the maximum voluntary contraction force (MVC) to 60% of the MVC. The research results show that as the hand force application increases, the effect of the muscle strength prediction model becomes worse. Then the ablation experiment is used to analyze the influence degree of each module on the muscle strength prediction result, and it is found that the CBAM module plays a key role in the model. Therefore, by using the model in this article, the accuracy of muscle strength prediction can be effectively improved, and the characteristics and laws of hand muscle activities can be deeply understood, providing assistance for further exploring the mechanism of hand functions .
Neural Networks, Computer ; Humans ; Hand Strength/physiology* ; Support Vector Machine ; Muscle Strength/physiology* ; Hand/physiology* ; Convolutional Neural Networks

Prosthesis loosening is the leading cause of postoperative revision in unicompartmental knee arthroplasty (UKA). The deviation of medial and lateral translational installation of the prosthesis during surgery is a common clinical phenomenon and an important factor in increasing the risk of prosthesis loosening. This study established a UKA finite element model and a bone-prosthesis fixation interface micromotion prediction model. The predicted medial contact force and joint motion of the knee joint from a patient-specific lower extremity musculoskeletal multibody dynamics model of UKA were used as boundary conditions. The effects of 9 femoral component medial and lateral translational installation deviations on the Von Mises stress of the proximal tibia, the contact stress, and the micro-motion of the bone prosthesis fixation interface were quantitatively studied. It was found that compared with the neutral position (a/A of 0.492), the lateral translational deviation of the femoral component significantly increased the tibial Von Mises stress and the bone-prosthesis fixation interface contact stress. The maximum Von Mises stress and the maximum contact stress of the fixation interface increased by 14.08% and 143.15%, respectively, when a/A was 0.361. The medial translational deviation of the femoral component significantly increased the bone-prosthesis fixation interface micro-motion. The maximum value of micromotion under the conditions of femoral neutral and medial translation deviation was in the range of 20-50 μm, which is suitable for osseointegration. Therefore, based on considerations such as the micromotion range suitable for osseointegration reported in the literature, the risk of reducing prosthesis loosening, and factors that may induce pain, it is recommended that clinicians control the mounting position of the femoral component during surgery within the safe range of 0-4 mm medial translation deviation.
Humans ; Arthroplasty, Replacement, Knee/methods* ; Finite Element Analysis ; Biomechanical Phenomena ; Knee Prosthesis ; Tibia/surgery* ; Femur/surgery* ; Stress, Mechanical ; Prosthesis Failure ; Knee Joint/surgery* ; Prosthesis Design

Aiming at the problems of low accuracy and large difference of segmentation boundary distance in anterior cruciate ligament (ACL) image segmentation of knee joint, this paper proposes an ACL image segmentation model by fusing dilated convolution and residual hybrid attention U-shaped network (DRH-UNet). The proposed model builds upon the U-shaped network (U-Net) by incorporating dilated convolutions to expand the receptive field, enabling a better understanding of the contextual relationships within the image. Additionally, a residual hybrid attention block is designed in the skip connections to enhance the expression of critical features in key regions and reduce the semantic gap, thereby improving the representation capability for the ACL area. This study constructs an enhanced annotated ACL dataset based on the publicly available Magnetic Resonance Imaging Network (MRNet) dataset. The proposed method is validated on this dataset, and the experimental results demonstrate that the DRH-UNet model achieves a Dice similarity coefficient (DSC) of (88.01±1.57)% and a Hausdorff distance (HD) of 5.16±0.85, outperforming other ACL segmentation methods. The proposed approach further enhances the segmentation accuracy of ACL, providing valuable assistance for subsequent clinical diagnosis by physicians.
Humans ; Magnetic Resonance Imaging/methods* ; Anterior Cruciate Ligament/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Knee Joint/diagnostic imaging* ; Neural Networks, Computer ; Algorithms ; Deep Learning

At present, upper limb motor rehabilitation relies on specific rehabilitation aids, ignoring the initiative of upper limb motor of patients in the middle and late stages of rehabilitation. This paper proposes a fuzzy evaluation method for active participation based on trajectory error and surface electromyography (sEMG) for patients who gradually have the ability to generate active force. First, the level of motor participation was evaluated using trajectory error signals represented by computer vision. Then, the level of physiological participation was quantified based on muscle activation (MA) characterized by sEMG. Finally, the motor performance and physiological response parameters were input into the fuzzy inference system (FIS). This system was then used to construct the fuzzy decision tree (FDT), which ultimately outputs the active participation level. A controlled experiment of upper limb flexion and extension exercise in 16 healthy subjects demonstrated that the method presented in this paper was effective in quantifying difference in the active participation level of the upper limb in different force-generating states. The calculation results of this method and the active participation assessment method based on sEMG during the task cycle showed that the active participation evaluation values of both methods peaked in the initial cycle: (82.34 ± 9.3) % for this paper's method and (78.44 ± 7.31) % for the sEMG method. In the subsequent cycles, the values of both showed a dynamic change trend of rising first and then falling. Trend consistency verifies the effectiveness of the active participation assessment strategy in this paper, providing a new idea for quantifying the participation level of patients in middle and late stages of upper limb rehabilitation without special equipment mediation.
Humans ; Electromyography/methods* ; Upper Extremity/physiology* ; Fuzzy Logic ; Exercise Therapy/methods* ; Muscle, Skeletal/physiology* ; Male

To solve the safety problems caused by the restriction of interaction space and the singular configuration of rehabilitation robot in terminal traction upper limb rehabilitation training, a trajectory planning and tracking control scheme for rehabilitation training is proposed. The human-robot safe interaction space was obtained based on kinematics modeling and rehabilitation theory, and the training trajectory was planned based on the occupational therapy in rehabilitation medicine. The singular configuration of the rehabilitation robot in the interaction space was avoided by exponential adaptive damped least square method. Then, a nonlinear controller for the upper limb rehabilitation robot was designed based on the backstepping control method. Radial basis function neural network was used to approximate the robot model information online to achieve model-free control. The stability of the controller was proved by Lyapunov stability theory. Experimental results demonstrate the effectiveness and superiority of the proposed singular avoidance control scheme.
Humans ; Upper Extremity ; Robotics/methods* ; Biomechanical Phenomena ; Neural Networks, Computer ; Traction/methods* ; Algorithms

Stroke causes abnormality of brain physiological function and limb motor function. Brain-computer interface (BCI) connects the patient's active consciousness to an external device, so as to enhance limb motor function. Previous studies have preliminarily confirmed the efficacy of BCI rehabilitation training in improving upper limb motor function after stroke, but the brain mechanism behind it is still unclear. This paper aims to review on the brain mechanism of upper limb motor dysfunction in stroke patients and the improvement of brain function in those receiving BCI training, aiming to further explore the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function after stroke. The results of this study show that in the fields of imaging and electrophysiology, abnormal activity and connectivity have been found in stroke patients. And BCI training for stroke patients can improve their upper limb motor function by increasing the activity and connectivity of one hemisphere of the brain and restoring the balance between the bilateral hemispheres of the brain. This article summarizes the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function in stroke in both imaging and electrophysiology, and provides a reference for the clinical application and scientific research of BCI in stroke rehabilitation in the future.
Humans ; Brain-Computer Interfaces ; Stroke Rehabilitation ; Upper Extremity/physiopathology* ; Brain/physiopathology* ; Electroencephalography ; Stroke/physiopathology*

The orientation of the acetabular cup in hip joint anteroposterior radiograph is a key factor in evaluating the postoperative outcomes of total hip arthroplasty (THA). Currently, measurement of the acetabular cup anteversion angle primarily relies on manual drawing of auxiliary lines by orthopedic surgeons and calculations using scientific calculators. This study proposes an automated computer-aided measurement method for the acetabular cup anteversion angle based on hip joint anteroposterior radiograph. The proposed method segments hip prosthesis images using an improved Otsu algorithm, identifies feature points at the acetabular cup opening by combining circle-fitting theory and the cup's geometric characteristics, and fits an ellipse to the cup opening to calculate the anteversion angle. A total of 104 hip joint anteroposterior radiographs, including 71 right-sided and 81 left-sided prostheses, were analyzed. Two orthopedic surgeons independently measured the postoperative anteversion angles, and the results were compared with computer-generated measurements for correlation analysis. Spearman and Pearson correlation analyses demonstrated significant correlations between the proposed method and manual measurements for both the right group ( r = 0.795, P < 0.01) and the left group ( r = 0.859, P < 0.01). This method provides a reliable reference for orthopedic surgeons to assess postoperative prognosis.
Humans ; Acetabulum/anatomy & histology* ; Arthroplasty, Replacement, Hip/methods* ; Algorithms ; Hip Prosthesis ; Hip Joint/diagnostic imaging* ; Radiography ; Image Processing, Computer-Assisted/methods*