Grading Method Research Articles

Fully Automatic Fine-Grained Grading of Lumbar Intervertebral Disc Degeneration Using Regional Feature Recalibration Network.

Accurate fine-grained grading of lumbar intervertebral disc (LIVD) degeneration is essential for the diagnosis and treatment design of high-incidence low back pain. However, the grading accuracy is still challenged by lacking the fine-grained degenerative details, which is mainly due to the existing grading methods are easily dominated by the salient nucleus pulposus regions in LIVD, overlooking the inconspicuous degeneration changes of the surrounding structures. In this study, a novel regional feature recalibration network (RFRecNet) is proposed to achieve accurate and reliable LIVD degeneration grading. Detection transformer (DETR) is first utilized to detect all LIVDs and then input to the proposed RFRecNet for the fine-grained grading. To obtain sufficient features from both the salient nucleus pulposus and the surrounding regions, a regional cube-based feature boosting and suppression (RC-FBS) module is designed to adaptively recalibrate the feature extraction and utilization from the various regions in LIVD, and a feature diversification (FD) module is proposed to capture the complementary semantic information from the multi-scale features for the comprehensive fine-grained degeneration grading. Extensive experiments were conducted on a clinically collected dataset, which consists of 500 MR scans with a total of 10225 LIVDs. An average grading accuracy of 90.5%, specificity of 97.5%, sensitivity of 90.8%, and Cohen's kappa correlation coefficient of 0.876 are obtained, which indicate that the proposed framework is promising to provide doctors with reliable and consistent fine-grained quantitative evaluation results of the LIVD degeneration conditions for the optimal surgical plan design.

Interventional Embolization of Unilateral Cavernous Sinus With ONXY Glue Combined With Coils for the Treatment of Bilateral Dural Arteriovenous Fistula.

To summarize the clinical experience of unilateral sponge sinus embolization of cavernous sinus dural arteriovenous fistula (CS DAVF) via jugular vein access with ONXY gel and spring coil intervention. The authors retrospectively analyzed the clinical data of 3 patients who were treated with unilateral cavernous sinus embolization for bilateral cavernous sinus dural arteriovenous fistula (CS DAVF) from October 2020 to May 2022 in our department by using transjugular vein access spring coil + ONXY gel interventional embolization. Immediate postoperative angiography suggested that the cavernous sinus fistula was completely embolized in all 3 patients, and cerebral angiography was carried out from 6 to 12 months after the operation and assessed using the Class grading method, and there was no recurrence in any of the 3 patients. Transvenous access ONXY adhesive combined with spring coil interventional embolization of unilateral cavernous sinus to cure bilateral cavernous sinus dural arteriovenous fistula is feasible, which can avoid the excessive use of spring coils and ONXY adhesive, and it is the key to the effective cure of DAVF in cavernous sinus area and to reduce the complication.

Deep Learning Model for Grading and Localization of Lumbar Disc Herniation on Magnetic Resonance Imaging.

Methods for grading and localization of lumbar disc herniation (LDH) on MRI are complex, time-consuming, and subjective. Utilizing deep learning (DL) models as assistance would mitigate such complexities. To develop an interpretable DL model capable of grading and localizing LDH. Retrospective. 1496 patients (M/F: 783/713) were evaluated, and randomly divided into training (70%), validation (10%), and test (20%) sets. 1.5T MRI for axial T2-weighted sequences (spin echo). The training set was annotated by three spinal surgeons using the Michigan State University classification to train the DL model. The test set was annotated by a spinal surgery expert (as ground truth labels), and two spinal surgeons (comparison with the trained model). An external test set was employed to evaluate the generalizability of the DL model. Calculated intersection over union (IoU) for detection consistency, utilized Gwet's AC1 to assess interobserver agreement, and evaluated model performance based on sensitivity and specificity, with statistical significance set at P < 0.05. The DL model achieved high detection consistency in both the internal test dataset (grading: mean IoU 0.84, recall 99.6%; localization: IoU 0.82, recall 99.5%) and external test dataset (grading: 0.72, 98.0%; localization: 0.71, 97.6%). For internal testing, the DL model (grading: 0.81; localization: 0.76), Rater 1 (0.88; 0.82), and Rater 2 (0.86; 0.83) demonstrated results highly consistent with the ground truth labels. The overall sensitivity of the DL model was 87.0% for grading and 84.0% for localization, while the specificity was 95.5% and 94.4%. For external testing, the DL model showed an appreciable decrease in consistency (grading: 0.69; localization: 0.66), sensitivity (77.2%; 76.7%), and specificity (92.3%; 91.8%). The classification capabilities of the DL model closely resemble those of spinal surgeons. For future improvement, enriching the diversity of cases could enhance the model's generalization. Stage 2.

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

A discrepancy-aware self-distillation method for multi-modal glioma grading

Gliomas are the most common intracranial primary tumors. Accurate grading of gliomas is crucial in determining treatment options and prognosis. Clinicians conventionally rely on multiple Magnetic Resonance Imaging (MRI) sequences for accurate glioma assessment. Traditional deep learning methods typically utilize the individual MRI sequence with the annotations of Region of Interest (ROIs), incurring substantial manual effort while losing the complementary information provided by other sequences. This task is inherently a standard multi-modal problem. However, existing methods often adopt complex multi-stream networks for feature extraction and fusion, leading to high resource demands and potential feature redundancy. To address the above challenge, a discrepancy-aware self-distillation method is proposed for multi-modal glioma grading, which requires only a single-stream network to concurrently analyze multiple MRI sequences without auxiliary ROIs. The first Modality Discrepancy-aware Fusion (MDF) module considers the MRI imaging differentiation and widens the inter-modal contrasts, allowing the modality-specific features to be highlighted and the modality-invariant features to be diminished. Furthermore, the proposed Class Activation Self-Distillation (CASD) strategy leverages the generated Class Activation Maps (CAMs) as dark knowledge for the distillation process. This guides shallow layers to focus on the category discriminative features specifically within the lesion region. Extensive experiments were conducted on the BraTS2018 and BraTS2019 datasets to evaluate the effectiveness of our method. The results show that our method outperforms other relevant glioma grading and multi-modal fusion methods on both datasets.

RELIABILITY OF CLINICAL GRADING OF DIABETIC RETINOPATHY COMPARED WITH GRADING OF ULTRA-WIDEFIELD IMAGES.

To evaluate the reliability of clinical grading of diabetic retinopathy (DR) severity compared with grading on ultra-widefield pseudocolor fundus (UWF-CF) and ultra-widefield fluorescein angiography (UWF-FA) images and their relative detection of sight-threatening DR and referable DR. A total of 184 diabetic eyes were analyzed. UWF-CF and UWF-FA images were graded based on the International Clinical Diabetic Retinopathy severity scale. Agreement between clinical and UWF-based severity grading was evaluated using Cohen's kappa coefficient. The rate of sight-threatening DR and referable DR was evaluated for each grading method. Moderate agreement was found between clinical grading and UWF-CF (k = 0.456, P < 0.001) and between UWF-CF and UWF-FA (k = 0.443, P < 0.001). The agreement between clinical grading and UWF-FA was fair (k = 0.397, P < 0.001). UWF-based grading identified a higher DR grade in 56 eyes (30%) on UWF-CF and 85 eyes (46.2%) on UWF-FA. Compared with clinical grading, UWF-FA detected a higher rate of sight-threatening DR (44%; 81/184 vs. 22.3%; 41/184), while UWF-CF detected more referable eyes (58.1%; 107/184 vs. 45.65%; 84/184). Ultra-widefield pseudocolor fundus is a valuable tool for identifying referable eyes and can be a useful, noninvasive adjunct to clinical grading. The results suggest that UWF-FA is particularly useful for detecting unsuspected sight-threatening DR in eyes with clinically referable DR.

- Mechanism of LDH and IL-8 Involved in pancreatic cancer Pain and the Correlation of Pain Degree

Objective: To observe the mechanism of lactate dehydrogenase (LDH) and interleukin 8 (IL-8) in pancreatic cancer pain and their correlation with pain degree. Methods: 126 patients with pancreatic cancer who visited our hospital from January 2021 to February 2023 were selected. The patients were divided into 58 patients with low pain (1~3 points) and 68 patients with high pain (4~10 points) by visual analog scale (VAS). And 50 health examinees in the same period were selected as the healthy control group. The serum LDH and IL-8 concentrations are analyzed by enzyme-linked immunosorbent assay, as well as the subjective pain grading method scoreis analyzed. The differences in LDH and IL-8 concentrations among the three groups of patients were compared. Pearson correlation analysis was used to investigate the correlation between LDH, IL-8 concentrations and patient pain. Binary logistic regression was used to determine independent risk factors for high pain, and ROC curves were used to analyze the diagnostic efficacy of each indicator. Result: The serum LDH and IL-8 concentrations in the high pain group were exceed the low pain group’s (P&lt;0.05). The serum LDH and IL-8 concentrations in the low pain groupexceed the healthy control group’s(P&lt;0.05). Pearson correlation analysis revealed a positive correlation between serum LDH concentration and pain grading (r=0.736, P=0.000). The serum IL-8 has positive correlation with pain grading (r=0.680, P=0.000). Serum LDH and IL-8 concentrations have positive correlation(r=0.589, P=0.000). LDH and IL-8 concentrations are independent risk factors for high pain levels (OR=1.033, 1.142, P&lt;0.05). The logistic regression prediction model formula was used: Y=constant+B1X1+B2X2+...+BnXn to set the joint diagnostic prediction model as -12.063+0.033×LDH+0.133×IL-8. The areas under the ROC curves of LDH, IL-8, and predictive model (LDH+IL-8) in patients with high pain were 0.925, 0.945, and 0.974, respectively. The relevant standards for LDH are&gt;190U/L, IL-8 is&gt;36pg/mL, and the relevant standards for prediction models are&gt;5.75. Conclusion: LDH and IL-8 participate in the pain aggravation process of pancreatic cancer, and are closely related to the pain grading. The combination of LDH and IL-8 can be used as a biological indicator to evaluate the pain severity of pancreatic cancer, and provide reference for clinical diagnosis and treatment.

Characterizing the Validity of Using VASES to Derive DIGEST-FEES Grades

Introduction: Visual Analysis of Swallowing Efficiency and Safety (VASES) and Dynamic Imaging Grade of Swallowing Toxicity for Flexible Endoscopic Evaluation of Swallowing (DIGEST-FEES) are two complimentary methods for assessing swallowing during FEES. Whereas VASES is intended to facilitate trial-level ratings of pharyngeal residue, penetration, and aspiration, DIGEST-FEES is intended to facilitate protocol-level impairment grades of swallowing safety and efficiency. The aim of this study was to assess the validity of using VASES to derive DIGEST-FEES impairment grades. Methods: DIGEST-FEES grades were blindly analyzed from 50 FEES – first using the original DIGEST-FEES grading method (n = 50) and then again using a VASES-derived DIGEST-FEES grading method (n = 50). Weighted Kappa (κw) and absolute agreement (%) were used to assess the relationship between the original DIGEST-FEES grades and VASES-derived DIGEST-FEES grades. Spearman’s correlations assessed the relationship between VASES-derived DIGEST-FEES grades with measures of construct validity. Results: Substantial agreement (κw = 0.76–0.83) was observed between the original and VASES-derived grading methods, with 60–62% of all DIGEST-FEES grades matching exactly, and 92–100% of DIGEST-FEES grades within one grade of each other. Furthermore, the strength of the relationships between VASES-derived DIGEST-FEES grades and measures of construct validity (r = 0.34–0.78) were similar to the strength of the relationships between original DIGEST-FEES grades and the same measures of construct validity (r = 0.34–0.83). Conclusion: Findings from this study demonstrate substantial agreement between original and VASES-derived DIGEST-FEES grades. Using VASES to derive DIGEST-FEES also appears to maintain the same level of construct validity established with the original DIGEST-FEES. Therefore, clinicians and researchers may consider using VASES to increase the transparency and standardization of DIGEST-FEES ratings. Future research should seek to replicate these findings and explore the simultaneous use of VASES and DIGEST-FEES in a greater sampling of raters and across other patient populations.

Performance analysis of Alexnet for Classification of Knee Osteoarthritis.

In recent years, automated grading of knee osteoarthritis (KOA) has focused on determining disease progression. Clinical examinations and radiographic image review are necessary for diagnosis. Timely and accurate diagnosis, along with medical care, can slow down KOA progression. X-rays and MRI are crucial diagnostic tools. KOA diagnosis traditionally relies on radiologists' and clinicians' experience. However, the rapid development of deep learning technology (AI) offers promising solutions for medical applications. The objective of this study was to review and summarize various methods proposed by researchers for the automated grading of KOA. Additionally, this study aimed to evaluate the performance of the AlexNet model in classifying the severity of KOA. The performance of the AlexNet model has been compared to that of other models, and the results have been assessed. A comprehensive review of existing research on automated grading of KOA has been conducted. Various methods proposed by different researchers have been examined and summarized. The AlexNet model has been employed for classifying the severity of KOA, and its performance has been evaluated. A comparative analysis has been carried out to compare the performance of the AlexNet model with that of other models. The results obtained from the evaluation have been assessed to determine the effectiveness of the AlexNet model in the automated grading of KOA. The results of the study indicate that the AlexNet model demonstrates promising performance in classifying the severity of KOA. Comparative analysis reveals that the AlexNet model outperforms other models in terms of accuracy and efficiency. The evaluation of the model's performance provides valuable insights into the effectiveness of deep learning techniques for automated grading of KOA. This study highlights the significance of automated grading in the diagnosis and management of knee osteoarthritis. The utilization of deep learning technology, particularly the AlexNet model, shows promise in accurately classifying the severity of KOA. The findings suggest that automated grading methods can serve as valuable tools for healthcare professionals in assessing the progression of KOA and providing appropriate medical care. Further research and development in this area can contribute to enhancing the efficiency and accuracy of automated grading systems for KOA.

Improving Grading Fairness and Transparency with Decentralized Collaborative Peer Assessment

Computer-assisted collaborative peer grading is a developing growth area in academic evaluation. However, peer assessment often needs help with problems such as the lack of reliability, transparency, fairness, grading speed, and motivation to participate among students. The literature suggests several principles that each partly address the said issues. We propose a novel decentralized approach to academic peer assessment, using blockchain as an underlying technology, to address the principal problems in traditional peer assessment. We also derive design concepts for a modern courseware (CW) application consisting of our method and apply them to implement our approach in a CW called Blockment. We test the effectiveness of our method and system by running quantitative and qualitative experiments, proving our claims of improving reliability, transparency, fairness, grading speed, and motivation of grades in peer assessment. The results suggest embedding our method and system in academic courses to improve conventional peer grading methods.

Visual interpretable MRI fine grading of meniscus injury for intelligent assisted diagnosis and treatment

Meniscal injury represents a common type of knee injury, accounting for over 50% of all knee injuries. The clinical diagnosis and treatment of meniscal injury heavily rely on magnetic resonance imaging (MRI). However, accurately diagnosing the meniscus from a comprehensive knee MRI is challenging due to its limited and weak signal, significantly impeding the precise grading of meniscal injuries. In this study, a visual interpretable fine grading (VIFG) diagnosis model has been developed to facilitate intelligent and quantified grading of meniscal injuries. Leveraging a multilevel transfer learning framework, it extracts comprehensive features and incorporates an attributional attention module to precisely locate the injured positions. Moreover, the attention-enhancing feedback module effectively concentrates on and distinguishes regions with similar grades of injury. The proposed method underwent validation on FastMRI_Knee and Xijing_Knee dataset, achieving mean grading accuracies of 0.8631 and 0.8502, surpassing the state-of-the-art grading methods notably in error-prone Grade 1 and Grade 2 cases. Additionally, the visually interpretable heatmaps generated by VIFG provide accurate depictions of actual or potential meniscus injury areas beyond human visual capability. Building upon this, a novel fine grading criterion was introduced for subtypes of meniscal injury, further classifying Grade 2 into 2a, 2b, and 2c, aligning with the anatomical knowledge of meniscal blood supply. It can provide enhanced injury-specific details, facilitating the development of more precise surgical strategies. The efficacy of this subtype classification was evidenced in 20 arthroscopic cases, underscoring the potential enhancement brought by intelligent-assisted diagnosis and treatment for meniscal injuries.

On the Quality and Validity of Course Evaluation Questionnaires Used in Tertiary Education in Greece

In compliance with national legislation, Greek tertiary education institutions assess educational quality often using a standardized anonymous questionnaire completed by students. This questionnaire aims to independently evaluate various course components, including content organization, instructor quality, facilities, infrastructure, and grading methods. Despite widespread use across universities, the questionnaire’s validity remains unexamined. This study addresses this gap by analyzing 48,000+ questionnaire responses from the University of the Peloponnese (2014–2022), encompassing 68 undergraduate and graduate programs. Confirmatory factor analyses were used to assess the quality of the questionnaire, while exploratory factor analyses were used to assess the dimensions of the tool based on the data. Both analyses reveal shortcomings: confirmatory analysis detects strong correlations between supposedly different factors, and exploratory analysis identifies dimensions inconsistent with the expected structure. These findings question the questionnaire’s quality and the validity of drawn conclusions, while additionally identifying opportunities for reducing the number of questions, which can contribute to increased questionnaire submission rates. Given its common use across Greek universities and its influence on shaping courses, urgent redesigning of the questionnaire for tertiary education evaluation is recommended.

Effectiveness of Conventional Digital Fundus Photography-Based Teleretinal Screening for Diabetic Retinopathy and Diabetic Macular Edema: A Report by the American Academy of Ophthalmology

This American Academy of Ophthalmology Ophthalmic Technology Assessment aims to assess the effectiveness of conventional teleretinal screening (TS) in detecting diabetic retinopathy (DR) and diabetic macular edema (DME). A literature search of the PubMed database was conducted most recently in July 2023 to identify data published between 2006 and 2023 on any of the following elements related to TS effectiveness: (1) the accuracy of TS in detecting DR or DME compared with traditional ophthalmic screening with dilated fundus examination or 7-standard field Early Treatment Diabetic Retinopathy Study photography, (2) the impact of TS on DR screening compliance rates or other patient behaviors, and (3) cost-effectiveness and patient satisfaction of TS compared with traditional DR screening. Identified studies then were rated based on the Oxford Centre for Evidence-Based Medicine grading system. Eight level I studies, 14 level II studies, and 2 level III studies were identified in total. Although cross-study comparison is challenging because of differences in reference standards and grading methods, TS demonstrated acceptable sensitivity and good specificity in detecting DR; moderate to good agreement between TS and reference-standard DR grading was observed. Performance of TS was not as robust in detecting DME, although the number of studies evaluating DME specifically was limited. Two level I studies, 5 level II studies, and 1 level III study supported that TS had a positive impact on overall DR screening compliance, even increasing it by more than 2-fold in one study. Studies assessing cost-effectiveness and patient satisfaction were not graded formally, but they generally showed that TS was cost-effective and preferred by patients over traditional surveillance. Conventional TS is an effective approach to DR screening not only for its accuracy in detecting referable-level disease, but also for improving screening compliance in a cost-effective manner that may be preferred by patients. Further research is needed to elucidate the ideal approach of TS that may involve integration of artificial intelligence or other imaging technologies in the future. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Clinical application of multicolor scanning laser ophthalmology in diagnosis and grading of central retinal artery occlusion.

To characterize features of central retinal artery occlusion (CRAO) using multicolor (MC) imaging and to assess the differences in CRAO grading between color fundus photography (CFP) and MC image qualitatively and quantitatively. We conducted a prospective, cross-sectional study in the Department of Ophthalmology of Renmin Hospital of Wuhan University. In total, 86 acute CRAO patients were included. Spectral-domain optical coherence tomography (SD-OCT), CFP, and MC examinations were taken at baseline. Based on the findings of these three examinations, CRAO was divided into three grades (incomplete, subtotal, and total). Based on OCT grading criteria, we qualitatively compared the ability of grading CRAO by CFP and MC. CRAO patient's visual acuity (VA) was obtained from the initial visit. The retinal thickness was measured by SD-OCT. Superficial capillary plexus (SCP) and deep capillary plexus (DCP) were obtained from optical coherence tomography angiography (OCTA) examinations. Quantitative data were compared across the three acute CRAO subgroups and against three examination findings. MC image had significantly higher power of acute CRAO detection than CFP (P = 0.03). In the same group of CRAO patients, there was no significant difference in VA when comparing OCT with the MC grading system or with the CFP grading system (all P > 0.05). Significant differences in VA were found between the three CRAO subgroups only under MC grading (P = 0.016). In incomplete CRAO patients, significant differences were found in central fovea thickness (CFT) when comparing OCT with the CFP grading system (P = 0.019). In the same group of CRAO patients, there was no significant difference in retinal thickness when comparing OCT with the MC grading system (All P > 0.05). Significance differences in CFT (P < 0.001), innermost retinal layer (IMRL; P < 0.01), middle retinal layer (MRL; P < 0.001), and outer retinal layer (ORL; P = 0.021) were found between the three CRAO subgroups by MC grading. Vessel density of SCP showed a statistically increased as the severity of three CRAO subgroups (P = 0.03), whereas DCP did not have significant differences (P = 0.745). Comparisons were made between the OCT grading method and the MC and CFP grading methods; there is no significant difference in vessel density of SCP and DCP (All P > 0.05). The images obtained by MC are superior to those obtained by CFP in CRAO grading, retinal thickness, and vessel density measurement. MC imaging may be more capable of CRAO grading than OCT. We recommend MC imaging to determine CRAO severity to guide disease treatment and predict visual prognosis.

Workplace interventions to reduce the risk of SARS-CoV-2 infection outside of healthcare settings.

Workplace interventions to reduce the risk of SARS-CoV-2 infection outside of healthcare settings.

Comparing meibomian gland visibility on optical coherence tomography and Keratograph 5M images using objective and subjective grading methods

PurposeTo investigate if there is a visible difference in meibomian gland (MG) length between images captured with the Visante optical coherence tomography (OCT; wavelength = 1,310 nm) and the OCULUS Keratograph 5M (K5M; wavelength = 880 nm). MethodsAdults between 18 and 40 years were recruited. Baseline dry eye disease was evaluated with the Standard Patient Evaluation of Eye Dryness (SPEED) and tear meniscus height and tear breakup time with the K5M. Right upper and lower eyelid MGs were imaged with the K5M and Visante OCT. Each image was graded with the 0 to 3 meiboscore scale. The central 5 MGs were evaluated with ImageJ for percent gland length visibility. ResultsThirty participants were analyzed with a median (interquartile range [IQR]) age of 23.0 (5.0) years (53.3 % female). Overall, participants were asymptomatic and had normal tear films. Meiboscores based on K5M and Visante OCT was significantly different for the lower eyelid (0[1] vs 1[2]; p = 0.007) but not the upper eyelid (0[1] vs 0[1]; p = 1.00). The mean percent gland visibility of the upper eyelid (82.7[9.6] vs 75.2[13.5]; p < 0.001) and the lower eyelid (81.2[12.7] vs 64.1[17.6]; p < 0.001) were significantly greater on the Visante OCT than the K5M images, respectively. ConclusionOCT images had significantly greater percent visible MG lengths than the K5M images. This suggests viable segments of the MGs may be missed with typical imaging, which may explain how it is possible that studies have found less post-treatment MG atrophy.

Effect of type III female genital mutilation on obstetric outcomes: A systematic review and meta-analysis

BackgroundControversial evidence suggests a potential association between female genital mutilation (FGM/C) and adverse obstetric outcomes, with type III FGM/C (infibulation) carrying the greatest risk. The aim of this systematic review and meta-analysis was to assess current rate of adverse obstetric outcomes in women with type III female genital mutilation and cutting (FGM/C; infibulation) delivering across different settings worldwide. MethodsWe searched PubMed, Scopus, Embase, and ClinicalTrials.gov databases from inception to Jan 1, 2023. Studies were selected if they included the main outcome of postpartum haemorrhage (PPH) or secondary outcomes, which included major conditions affecting maternal-neonatal health during labour and delivery. DerSimonian-Laird random effects meta-analysis including pooled effect estimates with corresponding 95 % confidence intervals was performed. Heterogeneity was assessed using the I2 statistic. Meta regression for relevant covariates was performed when data on relevant confounders were available. The Newcastle-Ottawa scale (NOS) was used to assess quality of observational studies. The level of evidence was assessed with the GRADE method. Results14 observational studies including 15,320 type III FGM/C women and 59,347 controls were eligible. The risk for postpartum haemorrhage was significantly increased in type III FGM/C, in the main analysis (OR 1.83, 95 % CI 1.03 to 3.24, I2 = 93 %), in pooling of data adjusted for confounders (aOR 1.76, CI 1.42 to 2.17, I2 = 0 %), and in sensitivity analysis of higher quality studies with NOS≥7 (OR 2.76, CI 1.38 to 5.51, I2 = 95 %). Meta-regression showed that nulliparity was significantly and positively associated with postpartum haemorrhage. Similarly, analysis of data adjusted for confounders showed an increased risk of episiotomy in type III FGM/C (aOR 1.56, CI 1.03 to 2.35, I2 = 52 %). Sensitivity analysis of studies with NOS≥7 revealed a significant increase for episiotomy (OR 7.53, CI 1.19 to 47.54, I2 = 96 %), perineal tears (OR 4.24, CI 1.09 to 16.46, I2 = 66 %), prolonged second stage of labour (OR 5.19, 95 % CI 1.00 to 26.85, I2 = 66 %), and Apgar score less than 7 (OR 4.19, CI 1.64 to 10.70, I2 = 0 %). No difference was found regarding obstetric anal sphincter injuries and mode of delivery in these women. Deinfibulation achieved similar obstetric and neonatal outcomes to women who never had type III FGM. The overall quality of the studies was adequate (median NOS score: 7; IQR: 6–8), the level of evidence, according to the GRADE assessment, was low. ConclusionsThese results consistently show an increased risk of adverse obstetric outcomes in women with FGM/C type III. Infibulation substantially increases the risk for PPH, particularly in nulliparae. Systematic Review registration: PROSPERO CRD42023421993.

Grading recommendations for enhanced patient safety in sentinel event analysis: the recommendation improvement matrix

ObjectivesThe goal of sentinel event (SE) analysis is to prevent recurrence. However, the rate of SEs has remained constant over the past years. Research suggests this is in part due...

An AFM-Based Model-Fitting-Free Viscoelasticity Characterization Method for Accurate Grading of Primary Prostate Tumor.

Viscoelasticity is a crucial property of cells, which plays an important role in label-free cell characterization. This paper reports a model-fitting-free viscoelasticity calculation method, correcting the effects of frequency, surface adhesion and liquid resistance on AFM force-distance (FD) curves. As demonstrated by quantifying the viscosity and elastic modulus of PC-3 cells, this method shows high self-consistency and little dependence on experimental parameters such as loading frequency, and loading mode (Force-volume vs. PeakForce Tapping). The rapid calculating speed of less than 1ms per curve without the need for a model fitting process is another advantage. Furthermore, this method was utilized to characterize the viscoelastic properties of primary clinical prostate cells from 38 patients. The results demonstrate that the reported characterization method a comparable performance with the Gleason Score system in grading prostate cancer cells, This method achieves a high average accuracy of 97.6% in distinguishing low-risk prostate tumors (BPH and GS6) from higher-risk (GS7-GS10) prostate tumors and a high average accuracy of 93.3% in distinguishing BPH from prostate cancer.

Cross-sectional study on the relationship between Body Mass Index and severity of knee osteoarthritis

Background. Musculoskeletal diseases like osteoarthritis (OA) are widespread. OA affects the knees, makes the joints wear out and hurt, and makes it hard to move around. Body mass index (BMI) is a way to measure obesity, which is linked to a higher chance of getting OA and making it worse. The main aim of this cross-sectional study was to find out how body mass index (BMI) is related to how lousy knee osteoarthritis is. Methods. One hundred people at the Shri Guru Ram Rai Institute of Medical and Health Sciences in Dehradun met the study’s requirements and were enrolled. The Kellgren-Lawrence grading method was used to find the severity of knee osteoarthritis (OA), and standard measurements were used to calculate body mass index (BMI). A correlation study was done to examine the link between BMI and the severity of OA. Results. The sample included 100 participants on average of 60 years old. The typical knee OA symptoms lasted five years. A body mass index distribution analysis showed 35% obese, 40% overweight, and 25% underweight or average weight. Twenty percent of knee OA patients were mild, 35 percent intermediate, 30 percent severe, and 15 percent progressed. The association investigation revealed a significant positive correlation between BMI and knee osteoarthritis severity (r = 0.65, p &lt; 0.001). Conclusion. Our results show that being overweight can worsen knee OA, proving that people with this problem need to watch their weight. If we treat obesity as a risk factor that can be changed, we might be able to improve the results of OA.