Interpreting Patient Reported Outcomes in Orthopaedic Surgery: A Systematic Review

Background: Reporting methods of patient reported outcome measures (PROMs) vary in orthopaedic surgery literature. While most studies report statistical significance, the interpretation of results would be improved if authors reported confidence intervals (CIs), the minimally clinically important difference (MCID), and number needed to treat (NNT). Objective: To assess the quality and interpretability of reporting the results of PROMs. To evaluate reporting, we will assess the proportion of studies that reported (1) 95% CIs, (2) MCID, and (3) NNT. To evaluate interpretation, we will assess the proportion of studies that discussed results using the MCID or the effect sizes and how they relate to 95% CIs. Methods: We included the top five high impact factor orthopaedic journals (The American Journal of Sports Medicine, Arthroscopy, Journal of Bone and Joint Surgery, The Journal of Arthroplasty, and Osteoarthritis and Cartilage) published in 2017, that compared two or more therapies, and used PROMs to report study outcomes. Three sets of independent reviewers participated in screening and data extraction using a standardized form. Results: Our search yielded 1502 studies. Following titles and abstracts screening, 254 studies remained. Following full text screening, 194 eligible studies were included in the final analysis. Data extraction is currently underway. Discussion: Results of trials using PROMs should be completely reported and correctly interpreted. The current trend of reporting results and basing conclusions solely on p-values can lead to inaccurate conclusions and clinical recommendations. Journal guidelines should consider mandating such values in future research

Using Multilayer Perceptron Neural Network to Assess the Critical Factors of Traffic Accidents

This study is based on the traffic accident data of Taoyuan City from the government's open data. The study compiled the data set of traffic accidents in Taiwan from 2012 to 2017, and six classifiers were applied to evaluate the effectiveness of traffic accident prediction with the number of injuries as the prediction target. In order to verify the classifier's stability, cross-validation was used to evaluate the model during the training process, and the multilayer perceptron neural network (MLPNN) classifier performed best in testing the dataset's accuracy and evaluating the model's best performance. Then, a boosting ensemble learning approach and a combination of traffic accident factors improve the experiment's performance. According to this experiment, the results show that this study uses the Pearson Chi-square feature selection method to select important traffic factor combinations, and the boosting method indeed helps improve the effectiveness of the construction of the traffic accident model. Finally, the experimental results of the NN-MLP model have a correct rate of 77% and AUC is 78.7%. In constructing the model, it was found that the degree of injury, the part of the vehicle hit, the type of accident, the leading cause, the type of vehicle, and the period of the accident were the main factors causing dangerous traffic accidents.   Doi: 10.28991/HIJ-2024-05-01-012 Full Text: PD

Review on antibacterial biocomposites of structural laminated veneer lumber

In this review, the characteristics and applications of structural laminated veneer lumber made from planted forest wood is introduced, and its preparation is explained, including various tree species and slab qualities, treatments for multiple effects and reinforced composites. The relevant factors in the bonding technology and pressing processes as well as the mechanical properties, research direction and application prospects of structural laminated veneer lumber made from planted forest wood are discussed

RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation

Retinal vessel segmentation is generally grounded in image-based datasets collected with bench-top devices. The static images naturally lose the dynamic characteristics of retina fluctuation, resulting in diminished dataset richness, and the usage of bench-top devices further restricts dataset scalability due to its limited accessibility. Considering these limitations, we introduce the first video-based retinal dataset by employing handheld devices for data acquisition. The dataset comprises 635 smartphone-based fundus videos collected from four different clinics, involving 415 patients from 50 to 75 years old. It delivers comprehensive and precise annotations of retinal structures in both spatial and temporal dimensions, aiming to advance the landscape of vasculature segmentation. Specifically, the dataset provides three levels of spatial annotations: binary vessel masks for overall retinal structure delineation, general vein-artery masks for distinguishing the vein and artery, and fine-grained vein-artery masks for further characterizing the granularities of each artery and vein. In addition, the dataset offers temporal annotations that capture the vessel pulsation characteristics, assisting in detecting ocular diseases that require fine-grained recognition of hemodynamic fluctuation. In application, our dataset exhibits a significant domain shift with respect to data captured by bench-top devices, thus posing great challenges to existing methods. In the experiments, we provide evaluation metrics and benchmark results on our dataset, reflecting both the potential and challenges it offers for vessel segmentation tasks. We hope this challenging dataset would significantly contribute to the development of eye disease diagnosis and early prevention

Comparative study on calcium, magnesium and cobalt in diabetic and non diabetic patients (males) in Punjab, Pakistan

Minerals are very important because of their important role in the maintenance of human health. Our study was aimed at assessing and comparing the levels of Ca, Mg and Co in the blood samples of diabetic and non diabetic persons (males) of five age groups between one and 75 years. For this purpose, fasting blood samples of diabetic and non diabetic males of selected age groups were collected. The blood samples were centrifuged to get serum. The mineral elements in the serum were analyzed by using atomic absorption spectrophotometer. The results depicted that the diabetic patients had higher mean concentration of Ca (141.47 ppm), Mg (18 ppm) and Co (0.057 ppm) while non diabetic persons had higher mean concentration of Ca (201.33 ppm), Mg (36.15 ppm) and Co (0.047 ppm). Statistical analysis was done by applying Student’s T-test. It can be concluded from the study that the diabetic patients of all five age groups are deficient of Ca, Mg, whereas the concentration of Co is higher in diabetic patients. In the further study physiological parameters along with other inorganic cofactors are being taken into consideration.Key words: Diabetes mellitus, blood serum, Ca, Mg, Co

TMEM27 Suppresses Tumor Development by Promoting Ret Ubiquitination, Positioning, and Degradation

The TMEM127 gene encodes a transmembrane protein of poorly known function that is mutated in pheochromocytomas, neural crest-derived tumors of adrenomedullary cells. Here, we report that, at single-nucleus resolution, TMEM127-mutant tumors share precursor cells and transcription regulatory elements with pheochromocytomas carrying mutations of the tyrosine kinase receptor RET. Additionally, TMEM127-mutant pheochromocytomas, human cells, and mouse knockout models of TMEM127 accumulate RET and increase its signaling. TMEM127 contributes to RET cellular positioning, trafficking, and lysosome-mediated degradation. Mechanistically, TMEM127 binds to RET and recruits the NEDD4 E3 ubiquitin ligase for RET ubiquitination and degradation via TMEM127 C-terminal PxxY motifs. Lastly, increased cell proliferation and tumor burden after TMEM127 loss can be reversed by selective RET inhibitors in vitro and in vivo. Our results define TMEM127 as a component of the ubiquitin system and identify aberrant RET stabilization as a likely mechanism through which TMEM127 loss-of-function mutations cause pheochromocytoma

Global, regional, and national burden of rheumatoid arthritis, 1990-2020, and projections to 2050:a systematic analysis of the Global Burden of Disease Study 2021

Background: Rheumatoid arthritis is a chronic autoimmune inflammatory disease associated with disability and premature death. Up-to-date estimates of the burden of rheumatoid arthritis are required for health-care planning, resource allocation, and prevention. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we provide updated estimates of the prevalence of rheumatoid arthritis and its associated deaths and disability-adjusted life-years (DALYs) by age, sex, year, and location, with forecasted prevalence to 2050. Methods: Rheumatoid arthritis prevalence was estimated in 204 countries and territories from 1990 to 2020 using Bayesian meta-regression models and data from population-based studies and medical claims data (98 prevalence and 25 incidence studies). Mortality was estimated from vital registration data with the Cause of Death Ensemble model (CODEm). Years of life lost (YLL) were calculated with use of standard GBD lifetables, and years lived with disability (YLDs) were estimated from prevalence, a meta-analysed distribution of rheumatoid arthritis severity, and disability weights. DALYs were calculated by summing YLLs and YLDs. Smoking was the only risk factor analysed. Rheumatoid arthritis prevalence was forecast to 2050 by logistic regression with Socio-Demographic Index as a predictor, then multiplying by projected population estimates. Findings: In 2020, an estimated 17·6 million (95% uncertainty interval 15·8–20·3) people had rheumatoid arthritis worldwide. The age-standardised global prevalence rate was 208·8 cases (186·8–241·1) per 100 000 population, representing a 14·1% (12·7–15·4) increase since 1990. Prevalence was higher in females (age-standardised female-to-male prevalence ratio 2·45 [2·40–2·47]). The age-standardised death rate was 0·47 (0·41–0·54) per 100 000 population (38 300 global deaths [33 500–44 000]), a 23·8% (17·5–29·3) decrease from 1990 to 2020. The 2020 DALY count was 3 060 000 (2 320 000–3 860 000), with an age-standardised DALY rate of 36·4 (27·6–45·9) per 100 000 population. YLDs accounted for 76·4% (68·3–81·0) of DALYs. Smoking risk attribution for rheumatoid arthritis DALYs was 7·1% (3·6–10·3). We forecast that 31·7 million (25·8–39·0) individuals will be living with rheumatoid arthritis worldwide by 2050. Interpretation: Rheumatoid arthritis mortality has decreased globally over the past three decades. Global age-standardised prevalence rate and YLDs have increased over the same period, and the number of cases is projected to continue to increase to the year 2050. Improved access to early diagnosis and treatment of rheumatoid arthritis globally is required to reduce the future burden of the disease. Funding: Bill &amp; Melinda Gates Foundation, Institute of Bone and Joint Research, and Global Alliance for Musculoskeletal Health.</p