Grading Method Research Articles

Design and Experimentation of a Machine Vision-Based Cucumber Quality Grader.

The North China type cucumber, characterized by its dense spines and top flowers, is susceptible to damage during the grading process, affecting its market value. Moreover, traditional manual grading methods are time-consuming and labor-intensive. To address these issues, this paper proposes a cucumber quality grader based on machine vision and deep learning. In the electromechanical aspect, a novel fixed tray type grading mechanism is designed to prevent damage to the vulnerable North China type cucumbers during the grading process. In the vision grading algorithm, a new convolutional neural network is introduced named MassNet, capable of predicting cucumber mass using only a top-view image. After obtaining the cucumber mass prediction, mass grading is achieved. Experimental validation includes assessing the electromechanical performance of the grader, comparing MassNet with different models in predicting cucumber mass, and evaluating the online grading performance of the integrated algorithm. Experimental results indicate that the designed cucumber quality grader achieves a maximum capacity of 2.3 t/hr. In comparison with AlexNet, MobileNet, and ResNet, MassNet demonstrates superior cucumber mass prediction, with a MAPE of 3.9% and RMSE of 6.7 g. In online mass grading experiments, the grading efficiency of the cucumber quality grader reaches 93%.

A Spectral Method for Identifiable Grade of Membership Analysis with Binary Responses.

Grade of membership (GoM) models are popular individual-level mixture models for multivariate categorical data. GoM allows each subject to have mixed memberships in multiple extreme latent profiles. Therefore, GoM models have a richer modeling capacity than latent class models that restrict each subject to belong to a single profile. The flexibility of GoM comes at the cost of more challenging identifiability and estimation problems. In this work, we propose a singular value decomposition (SVD)-based spectral approach to GoM analysis with multivariate binary responses. Our approach hinges on the observation that the expectation of the data matrix has a low-rank decomposition under a GoM model. For identifiability, we develop sufficient and almost necessary conditions for a notion of expectation identifiability. For estimation, we extract only a few leading singular vectors of the observed data matrix and exploit the simplex geometry of these vectors to estimate the mixed membership scores and other parameters. We also establish the consistency of our estimator in the double-asymptotic regime where both the number of subjects and the number of items grow to infinity. Our spectral method has a huge computational advantage over Bayesian or likelihood-based methods and is scalable to large-scale and high-dimensional data. Extensive simulation studies demonstrate the superior efficiency and accuracy of our method. We also illustrate our method by applying it to a personality test dataset.

Spinal osteoarthritis is a risk of vertebral fractures in postmenopausal women

Recent studies have revealed that despite high bone mineral density (BMD), osteoarthritis (OA) is a risk factor for osteoporotic fractures. However, the relationship between spinal OA and vertebral fractures has not yet been fully investigated. This longitudinal analysis used a subset of ongoing cohort study consist with Japanese postmenopausal women. The prevalence of spinal OA was determined using Kellgren–Lawrence grading method. The incidence of vertebral fractures were determined by semiquantitative analysis of spinal X-ray films. The relationship between the presence of spinal OA and incidence of vertebral fractures was evaluated using the Cox regression analysis. In total, 1480 women were followed up for 8.1 ± 6.4 years. Among them, 923 were diagnosed with spinal OA, and incident vertebral fractures were observed in 473 participants. After adjusting for confounding variables, the spinal OA (≥ grade 2) was a significant predictor of incident vertebral fractures (hazard ratio, 1.52; 95% confidence interval: 1.19–1.93, p = 0.001). Using ROC analysis, the thresholds of lumbar BMD for incident vertebral fractures were 0.952 g/cm2 for patients with spinal OA and 0.753 g/cm2 for patients without spinal OA. The presence of spinal OA is a risk factor for incident vertebral fractures despite high lumbar BMD.

Periodontal disease severity in patients with long COVID and non-COVID-19

Background: Previous research studies have found the persistence of various COVID-19 symptoms even after the patient tested negative on a PCR test; this incident is now known as long COVID. These long COVID symptoms are reported to appear in the oral cavity including long COVID effects on periodontal disease, as both long COVID and periodontal disease release similar proinflammatory cytokines such as Acute phase proteins, CRP, TNF-α, IL-1β, IL-2, IL-6, and IFN-g. Purpose: This study aims to show periodontal-disease severity-frequency distribution in COVID-19 survivors with long COVID and in non-COVID-19 patients. Methods: Patients’ secondary data in the Periodontics Clinic Faculty of Dentistry at Trisakti University Dental Hospital (n=40) consisted of 20 samples from COVID-19 survivors who experienced long COVID and 20 samples from the non-COVID-19 group selected according to the inclusion criteria. Afterward, the data was recapitulated and processed into a research report. Results: The distribution percentage of generalized gingivitis was highest in non-COVID-19 patients, while generalized periodontitis was highest in COVID-19 survivors with long COVID. Based on periodontitis staging and grading methods, it is not proven that long COVID increases the severity of the periodontitis. Conclusion: This research shows that the distribution of gingivitis in COVID-19 survivors with long COVID has not increased. Meanwhile, the distribution of general periodontitis increased in survivors with long COVID. However, there was no increased severity of periodontitis based on the staging and grading method of periodontitis in the COVID-19 survivors with long COVID.

The second-line treatment of hepatocellular carcinoma with CalliSpheres drug-eluting bead transarterial chemoembolization combined with regorafenib: A safety and efficacy analysis.

This study aimed to investigate the efficacy and safety of CalliSpheres drug-eluting beads transarterial chemoembolization (DEB-TACE) combined with regorafenib in the second-line treatment of unresectable hepatocellular carcinoma. A retrospective analysis was made of 34 patients with unresectable hepatocellular carcinoma (HCC) that had progressed after first-line treatment in Linyi Tumor Hospital from October 2019 to June 2021. These patients were divided into observation group (n = 15) and control group (n = 19) based on their treatment plans, who were respectively treated with regorafenib alone and regorafenib combined with DEB-TACE. The objective response rate (ORR) and the disease control rate (DCR) were evaluated by the modified Response Evaluation Criteria in Solid Tumors (mRECIST), and the progression-free survival (PFS) and the overall survival (OS) were calculated; the factors influencing PFS and OS of patients were analyzed by the Cox proportional hazards model; and the adverse reactions to the treatments were observed and recorded. After 2months of treatment, the ORR and the DCR of the observation group were 73.3% (11/15) and 86.7% (13/15) respectively, both higher than 10.5% (2/19) and 47.4% (9/19) of the control group. Their differences are statistically significant (P < 0.05). There were no statistically significant differences in the incidences of regorafenib-related adverse reactions including hand-foot skin reactions, fatigue, hypertension, diarrhea, and proteinuria between the two groups (P > 0.05). In the observation group, the main adverse reactions to DEB-TACE such as fever, pain, nausea, and vomiting were relieved after symptomatic treatment, and no serious complications such as ectopic embolization of CalliSpheres drug-eluting beads occurred. As of July 31, 2022, the median follow-up time was 12.5months, and the average was (14.00 ± 5.69) months. The median PFS was 9months in the observation group, and 6months in the control group, presenting a statistically significant difference (P < 0.05), and the median OS was 18months in the observation group, and 12months in the control group, also presenting a statistically significant difference (P < 0.05). The results of monofactor prognostic analysis showed that Child grade, AFP level, and treatment method had an influence on the PFS and the OS of liver cancer patients receiving regorafenib second-line treatment (P < 0.05), and the results of multifactor prognostic analysis showed that child grade and treatment method independently influenced the PFS of patients, while treatment method independently influenced the OS of patients (P < 0.05). DEB-TACE combined with regorafenib is safe and feasible in the treatment of unresectable HCC, with good efficacy and mild adverse reactions.

Simulation analysis and experimental testing of the grading performance of small potato grading device

AbstractExisting potato grading device has a complex structure and a large volume, which is not suitable for small‐scale potato farmers. However, traditional manual grading methods not only have low efficiency but also high labor costs. This article developed a small movable rotating plate (MRP)‐type potato grading device, and the influences of three factors (the height of the horizontal slide, the tilt angle of the inclined slide, and the chain speed) on the grading efficiency and accuracy were investigated using the discrete element method and virtual orthogonal experiments. It was found that the horizontal slide height of 185 mm, the inclined slide tilt angle of 3.5°, and the chain speed of 0.9 m/s yielded the optimal grading efficiency (13.84 t/h) and grading accuracy (94.11%). Finally, the potato grading process was tested and validated through indoor tests, and the optimal grading accuracy was 91.04%. The grading accuracy deviation between the test and simulation results was less than 8%.Practical ApplicationCurrently, most commercial potato grading machines are designed for large‐scale farms, which are not suitable for developing countries. The potato grading device designed in this article can not only be used independently by small‐scale farms, but also be integrated with potato combine harvesters to achieve potato harvest and grading joint operations. This grading device has a simple structure, small footprint (1700 × 1200 × 840 mm3), and a very low cost ($600). With a high grading efficiency and accuracy, it can significantly improve the grading productivity of small and medium‐sized potato farmers in developing countries, reduce the production costs, and save labors.

Pre-processing technique of Aquilaria species from Malaysia for four different qualities

The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.

Meta-analysis of randomized clinical trials comparing robotic versus laparoscopic surgery for mid-low rectal cancers

Robotic surgery (RS) is experiencing major development, particularly in the context of rectal cancer. The aim of this meta-analysis was to summarize data from the literature, focusing specifically on the safety and effectiveness of robotic surgery in mid-low rectal cancers, based on the hypothesis that that robotic surgery can find its most rational indication in this anatomical location. The meta-analysis was conducted according to the PRISMA 2000 recommendations, including all randomized trials that compared robotic surgery versus laparoscopic surgery (LS) that were found in the Medline-PICO, Cochrane Database, Scopus and Google databases. Data were extracted independently by two reviewers. The risk of bias was analyzed according to the Cochrane Handbook method and the certainty of the evidence according to the GRADE method. The analysis was carried out with R software Version 4.2-3 using the Package for Meta-Analysis "meta" version 6.5-0. Eight randomized trials were included (with a total of 2342 patients), including four that focused specifically on mid-low rectal cancer (n=1,734 patients). No statistically significant difference was found for overall morbidity, intra-operative morbidity, anastomotic leakage, post-operative mortality, quality of mesorectal specimen, and resection margins. The main differences identified were a lower conversion rate for RS (RR=0.48 [0.24-0.95], p=0.04, I2=0%), and a longer operative time for RS (mean difference=39.11min [9.39-68.83], p<0.01, I2=96%). The other differences had no real clinical relevance, i.e., resumption of flatus passage (5hours earlier after RS), and lymph node dissection (one more lymph node for LS). This meta-analysis does not confirm the initial hypothesis and does not show a statistically significant or clinically relevant benefit of RS compared to LS for mid-low rectal cancer.

The management of diabetic foot: the new guidelines of the International Working Group on Diabetic Foot

. The management of diabetic foot: the new guidelines of the International Working Group on Diabetic Foot. The publication of the new guidelines on the diabetic foot are an opportunity for a fine-tuning of the (few) new developments, but of the soundness of the evidence and knowledge known so far. Compared to the previous 2019 guidelines, the 2023 update included the analysis of randomised clinical trials only, a more accurate application of the GRADE method, a leaner and more current bibliography, and an update of the strength of some recommendations from low to conditional. The real big news in the 2023 update is the publication of a specific guideline for the diagnosis and treatment of Charcot neuro-osteoarthropathy in people with diabetes, neuropathy and in the absence of skin lesions.

Establish a Graded Method to Avoid HLA Class I Antibodies Corresponding Antigen and Combining HLAMatchmaker Application in Improving the CCI Value after Platelet Transfusion for Patients with IPTR

To establish a graded method to avoid mean fluorescence intensity (MFI) threshold of HLA Class I antibodies corresponding antigen, and the HLAMatchmaker program has been used to select the minimum mismatch value of donor-patient epitopes. Evaluate the application value of combining both methods in selecting HLA compatible platelets (PTL) for patients with immune platelet transfusion failure (IPTR) in improving platelet the corrected count increment (CCI). A total 7 807 PLT cross-matching compatible were performed by the solid-phase red cell adherence (SPRCA) method for 51 IPTR patients. The Luminex single antigen flow cytometry was used to detect HLA Class I antibodies in patients, and detected the MFI value for different specificity antigens of HLA Class I antibodies, was graded into strong positive group (MFI>4 000, level 1), medium positive group (1 000< MFI≤4 000, 2), weak positive group (500< MFI≤1 000, 3), and one negative control group (MFI≤500). The results of 7 807 SPRCA their negative/positive reaction wells were enrolled and statistically analyzed in different grades and the four groups, the statistical differences between the four groups were compared. Multiple applications for the select HLA Class I compatible donor events were made for patients in two cases, and HLAMatchmaker program was used to calculate the number of HLA Class I epitopes mismatches between the donors and patients. The donor with the minimum number of epitopes mismatches was selected, while avoiding the corresponding antigens of HLA Class I antibodies in levels 1 and 2, the provision of HLA compatible platelets for IPTR. After the transfusions, the CCI value of the platelet transfusion efficacy evaluation index was calculated, and the clinical evaluation of the transfusion effect was obtained through statistical analysis. There were statistically significant differences in the positive results of SPRCA immunoassay among the strong positive group, medium positive group, and weak positive group of 51 IPTR patients with different specific of HLA -I class antibodies and corresponding antigens(all P <0.001). The positive results showed a range from high to low, with strong positive group>medium positive group>weak positive group. There were a statistical difference among between the strongly positive or moderately positive groups and the negative control group(P <0.001). There was no statistical difference between the weakly positive group and the negative control group(P >0.05). The strong positive group was set as the corresponding specific HLA Class I site corresponding antigen grade 1 avoidance threshold, the medium positive group as the grade 2 avoidance thresholds, and the weak positive group as the grade 3 avoidance threshold. In the case of donor platelet shortage, it is not necessary to avoid the weak positive group. Avoiding the strategy of donor antigens and HLAMatchmaker program scores ≤7 corresponding to HLA Class I antibodies of levels 1 and 2, with CCI values>4.5×109/L within 24 hours, can obtain effective clinical platelet transfusion conclusions. When selecting HLA Class I compatible donors for IPTR patients, the grading avoids HLA Class I antibodies corresponding to donor antigens, and the donor selection strategy with the minimum scores of HLAMatchmaker program is comprehensively selected. The negative result confirmed by platelet cross-matching experiments has certain practical application value for improving platelet count in IPTR patients.

Pattern analysis on &lt;em&gt;Aquilaria Malaccensis&lt;/em&gt; using machine learning

Aquilaria Malaccensis was found to generate agarwood. Because of its multiple benefits, agarwood essential oil, sometimes known as “black gold” is highly regarded universally. There is currently no accepted method for classifying various grades of agarwood essential oil. Due to the fact that the agarwood essential oil is assessed using a human sensory panel, the existing grading method is ineffective. Since different people may have different viewpoints on how to grade agarwood essential oil, it is not practical to apply the method universally. Several innovative methods for determining the classification of agarwood essential oil have been proposed and put into practise as a result of advanced technology. The study has constructed a pattern analysis on different grades of agarwood essential oil using 2D scatter plot. The results successfully indicate the scatter plots are scattered groupedly.

Reliability Issues in Standardized Assessment: A Study of the SSC English Examinations in Khyber Pakhtunkhwa

This research attempted to examine the reliability issues in the standardized assessment of the SSC English examinations in Khyber Pakhtunkhwa. Discrepancies in students’ achieved score on the examinations and their actual scores raise questions about the grading of the said English examinations. This study employed a survey with open-ended questions. The data were collected from students who scored 90% or above marks on the examinations through random sampling. The data were analyzed both qualitatively and quantitatively. The preliminary findings revealed a significant discrepancy between students' grades and their actual writing proficiency as identified through inter-rater grading. The study revealed a noticeable depreciation in inter-rater grading compared to the Board grades, thereby creating doubts about the reliability of the grading practices of the standardized English assessment. It is suggested that paper grading methods and techniques of board examinations are revisited.

Computational Thinking (CT) towards Creative Action: Developing a Project-Based Instructional Taxonomy (PBIT) in AI Education

This paper aims to develop a new model of Project-Based Instructional Taxonomy (PBIT) that provides a tool of course design that facilitates Computational Thinking (CT) development as creative action in solving real-life problems. Theoretically, PBIT is built on an integrative framework bringing together with studies on CT education, creativity, Bloom’s Taxonomy, and Project-Based Instruction (PBI). This guides the course design to make alignment between diverse elements including education objectives, categories of CT, levels of learning ability, process of project facilitation, and methods of grading. A case will be discussed that focuses on a course Deep Learning and Technologies in AI bachelor program at Northeastern University (NEU) in China. It also shows how PBIT is applied in teaching practice that benefits students’ CT development. As the conclusion indicates, this paper has contributions to future research on creativity, PBI, CT, and AI education.

Screening of positive dysphotopsia before multifocal intraocular lens implantation.

To assess the photic phenomena (PP) and positive dysphotopsia in candidates for presbyopia or cataract surgery and to evaluate their relationship with cataract grading systems. Retrospective observational. Monocular data for 82 subjects measured during the preoperative screening were retrospectively retrieved from our database. The evaluated variables consisted of two methods for PP measurement: light distortion index (LDI) and parameters obtained from a simulator, both of which were combined with subjective bother related to PP. The cutoff for LDI that better predicted patients passing from slightly to moderately bothersome was estimated. The relationships between LDI and the following objective cataract grading methods were also assessed: objective scatter index (OSI), dysfunctional lens index (DLI), and Pentacam Nucleus Staging (PNS). LDI was the best method for measuring PP, which showed a significant correlation with the bothersome question (rho = 0.34, P = 0.002) and also with OSI (rho = 0.67, P < 0.0005), DLI (rho = -0.29, P = 0.007), and PNS (rho = 0.48, P < 0.0005). The number/percentage of patients who found it bothersome was as follows: "Not at all" (18/22%), "Slightly" (41/50%), "Moderately" (15/18.3%), and "Very" (8/9.7%). The cutoff value that predicted the transition from slightly to moderately bothersome was ≥15.20% according to LDI, which could be estimated with the following values for grading: ≥2.8 for OSI, ≤7.6 for DLI, and ≥2 for PNS. Patients reporting moderately or higher bothersome levels in the preoperative period and with LDI <15.20%, <2.8 for OSI, >7.6 for DLI, and <2 for PNS might deserve special attention in the multifocal intraocular lens selection.

Efficacy of inhaled nitric oxide in preterm infants ≤ 34weeks: a systematic review and meta-analysis of randomized controlled trials.

Background: The effect of inhaled nitric oxide (iNO) in neonates >34 weeks on improving respiration is well documented. However, the efficacy of iNO in preterm infants ≤34 weeks remains controversial. Objectives: The main purpose of this review is to assess the effectiveness and safety of iNO treatment in preterm infants ≤34 weeks. Search methods: We systematically searched PubMed, Embase and Cochrane Libraries from their inception to 1 June 2023. We also reviewed the reference lists of retrieved studies. Selection criteria: Our study involved randomized controlled trials on preterm infants ≤34 weeks, especially those receiving iNO treatment, and mainly assessed outcomes such as bronchopulmonary dysplasia (BPD) and mortality. Two authors independently reviewed these trials, extracted data, and evaluated study biases. Disagreements were resolved by consensus. We used the GRADE method to assess evidence quality. Results: Our research included a total of 17 studies involving 4,080 neonates and 7 follow-up studies. The synthesis of results showed that in neonates, iNO treatment reduced the incidence of BPD (RR: 0.92; 95% CI: 0.86-0.98). It also decreased the composite outcome of death or BPD (RR: 0.94; 95% CI: 0.90-0.98), without increasing the risk of short-term (such as intraventricular hemorrhage, periventricular leukomalacia) and long-term neurological outcomes (including Bayley mental developmental index <70, cerebral palsy and neurodevelopmental impairment). Furthermore, iNO did not significantly affect other neonatal complications like sepsis, pulmonary hemorrhage, necrotizing enterocolitis, and symptomatic patent ductus arteriosus. Subgroup analysis revealed that iNO significantly reduced BPD incidence in neonates at 36 weeks under specific intervention conditions, including age less than 3 days, birth weight over 1,000g, iNO dose of 10ppm or higher, or treatment duration exceeding 7 days (p < 0.05). Conclusion: Inhaled NO reduced the incidence of BPD in neonates at 36 weeks of gestation, and the effect of the treatment depended on neonatal age, birth weight, duration and dose of iNO. Therefore, iNO can be considered a promising treatment for the potential prevention of BPD in premature infants. More data, however, would be needed to support nitric oxide registration in this specific patient population, to minimize its off-label use.

An improved method for extracting essay tangency features in intelligent scoring of English essays

With the continuous improvement of teaching quality in China's colleges and universities, English teaching has received more and more attention, and the automatic scoring system for English composition has begun to be gradually applied in English teaching in colleges and universities, but the system can only score compositions objectively, which is difficult to meet the actual teaching needs. Therefore, this study first proposes a topic richness based approach for analyzing composition cut points, and then proposes a combination based automatic grading method for English compositions. Based on the above, an intelligent grading improvement method based on composition tangent feature extraction is proposed to improve the efficiency of the English composition grading system and reduce the teaching burden on teachers. The traditional grading method is to grade grammar or essays by introducing Analytic Hierarchy Process, artificial neural networks, or combining machine learning theory with multi feature fusion. However, based on the characteristics of single semantic similarity and keyword alignment, the comprehensiveness of the grading is insufficient, making it difficult to analyze the correlation between essay sentences and the overall theme. The experimental results show that the topic decision-making model proposed in this study has better deep learning performance, and its scoring accuracy shows an upward trend with the increase of the sample data size of the essay, increasing from 40.05% to 91.46%. And the main content of the learned is the irrelevant information of the first sentence of a new topic and the truncation point, which can effectively distinguish whether a sentence is a topic segmentation point, and effectively identifies whether two sentences belong to the same topic; the proposed topic segmentation model has better practicality and its ability to segment topics is stronger; the combined method proposed in this study The overall performance of the model is better than the composition scoring methods based on RNN and BiLSTM models. Under the same semantic vectorization processing operation, the per values present in the composition sets are all different, and this result indicates that the writers' ability and the difficulty of writing on the topics are all different. It is hoped that this study can provide some solution ideas for the research of intelligent scoring feature extraction methods in English.

Research on the Application of Intelligent Grading Method based on Improved ML Algorithm in Sustainable English Education

The current intelligent grading methods in English education have issues of feedback lag and incompatibility with manual grading, and manual grading is easily influenced by subjective consciousness. Based on this, this research selects Adaboost algorithm in ML algorithm to realize intelligent grading. The experiment improves the Adaboost algorithm according to the actual needs, constructs the Adaboost/CT algorithm, and verifies its effectiveness. The experimental results show that in the English intelligent scoring module, the adjacency accuracy of the Adaboost/CT algorithm for high- and low-quality English is 95.33%; 95.45% in the middle score; 94% in the breakdown. The comparison between Adaboost/CT model and DFA method shows that the accuracy and proximity accuracy of Adaboost/CT are 79.66% and 94% respectively, which are much higher than 55% and 92% of DFA. In addition, compared with Adaboost, the accuracy of Adaboost/CT is also significantly better than Adaboost. In practical application, the use of Adaboost/CT in the evaluation of English compositions can not only get more accurate scores, but also find out the shortcomings of each student, so as to improve them pertinently. Meanwhile, the accuracy, recall, and F1 values of the Adaboost/CT algorithm are 96%, 95%, and 95%, respectively, which are higher than those of the comparison algorithm. Overall, the improved Adaboost/CT algorithm has shown high effectiveness and practicality, and has high applicability and effectiveness in practical intelligent grading.

Effects of physical exercise on executive functions of individuals with schizophrenia spectrum disorders: Protocol for a systematic review and meta-analysis.

Executive functions are commonly impaired in individuals with schizophrenia spectrum disorders. Physical exercise has the potential for improving executive functions and can be easily implemented as a therapeutic method. However, there are only few systematic reviews of exercise effects in schizophrenia including cognitive outcomes, and no meta-analytical syntheses of effects on "cool" and "hot" executive functions. The purpose of this systematic review and meta-analysis will be to determine the effects of physical exercise on "cool" and "hot" executive functions of adults with schizophrenia spectrum disorders. This protocol was guided by PRISMA-P guidelines. Studies will be searched using combinations of keywords and medical terms in the Web of Science, PubMed, Scopus, and EBSCO databases. Inclusion criteria will be determined as per PICOS approach. The risk of bias will be assessed using the Cochrane RoB2 tool. The certainty of evidence (per outcome) will be assessed using the GRADE method. The meta-analyses will be performed using the DerSimonian and Laird random effects model. Effect sizes (Hedges' g) with 95% confidence intervals will be calculated for each main outcome. The results of this review may be useful for mental health professionals to design treatment plans for adults with schizophrenia spectrum disorders, offering potential benefits related to the quality of life and cognitive abilities of this population. CRD42023392295.

Consistency of Radiological Grading of Cervical Foraminal Stenosis

Background:: The degree of cervical foraminal stenosis on MRI scans may be measured and categorised using the Kim or modified Kim methods. These grading scales have not previously been validated in a cohort of patients awaiting surgery. Objectives:: To establish the normal foraminal and root diameters as well as the consistency of inter and intra-rater grading using the Kim and modified Kim grading systems in pre-operative surgical patients. Methods:: Asymptomatic cervical nerve roots and foramina demonstrated on the pre-operative MRI scans of adult surgical patients with cervical radiculopathy were measured and categorised by six raters using the Kim and modified Kim grading methods. Repeat “second pass” measurements were made by the same assessors on the same images a minimum of one month later. Results:: Foraminal diameters (mm) in asymptomatic foramina were C2/C3 (mean±SD): 4.18±1.44, C3/C4 2.96±1.23, C4/C5 3.02±1.19, C5/C6 3.15±1.33, C6/C7 3.53±1.36, C7/T1 3.93±1.34. Nerve root diameters were C3 3.11±0.87, C4 2.95±0.77, C5 2.56±0.73, C6 2.26±0.76, C7 2.56±0.82, C8 3.83±0.86. Inter-rater consistency was kappa [95% CI]: Kim 0.01 [0.00, 0.03], modified Kim 0.08 [0.05, 0.10]. Intra-rater consistency was kappa [95% CI]: Kim 0.81 [0.77, 0.86], modified Kim 0.69 [0.62, 0.76]. Conclusion:: There was poor inter-rater consistency but good intra-rater consistency when assessing the severity of foraminal stenosis on axial T2 MRI scans. Foraminal diameter was narrowest at C3/C4 and C4/C5, whereas the smallest root diameter was C5/C6. Volumetric or oblique MR may improve consistency.

An automatic glaucoma grading method based on attention mechanism and EfficientNet-B3 network.

Glaucoma infection is rapidly spreading globally and the number of glaucoma patients is expected to exceed 110 million by 2040. Early identification and detection of glaucoma is particularly important as it can easily lead to irreversible vision damage or even blindness if not treated with intervention in the early stages. Deep learning has attracted much attention in the field of computer vision and has been widely studied especially in the recognition and diagnosis of ophthalmic diseases. It is challenging to efficiently extract effective features for accurate grading of glaucoma in a limited dataset. Currently, in glaucoma recognition algorithms, 2D fundus images are mainly used to automatically identify the disease or not, but do not distinguish between early or late stages; however, in clinical practice, the treatment of early and late glaucoma is not the same, so it is more important to proceed to achieve accurate grading of glaucoma. This study uses a private dataset containing modal data, 2D fundus images, and 3D-OCT scanner images, to extract the effective features therein to achieve an accurate triple classification (normal, early, and moderately advanced) for optimal performance on various measures. In view of this, this paper proposes an automatic glaucoma classification method based on the attention mechanism and EfficientNetB3 network. The EfficientNetB3 network and ResNet34 network are built to extract and fuse 2D fundus images and 3D-OCT scanner images, respectively, to achieve accurate classification. The proposed auto-classification method minimizes feature redundancy while improving classification accuracy, and incorporates an attention mechanism in the two-branch model, which enables the convolutional neural network to focus its attention on the main features of the eye and discard the meaningless black background region in the image to improve the performance of the model. The auto-classification method combined with the cross-entropy function achieves the highest accuracy up to 97.83%. Since the proposed automatic grading method is effective and ensures reliable decision-making for glaucoma screening, it can be used as a second opinion tool by doctors, which can greatly reduce missed diagnosis and misdiagnosis by doctors, and buy more time for patient's treatment.