Training Group Research Articles

Comparison of In-person VS remote learning modalities for ultrasound-guided knee arthrocentesis training using formalin-embalmed cadavers

ObjectivesThis study aims to compare the efficacy of remote versus in-person training strategies to teach ultrasound guided knee arthrocentesis using formalin embalmed cadavers.Methods30 first-year medical student participants were randomly assigned to remote or in-person training groups. Pre- and post- training surveys were used to evaluate participant’s self-confidence in their ability to perform the procedure. Participants were asked to watch a 30-minute training video and then attend a skills training workshop. The workshops consisted of 20 min of hands-on instruction followed by a skills assessment.ResultsFollowing training, participant self-confidence increased significantly across all survey items in both groups (p = 0.0001). No significant changes in participant self-confidence were detected between the groups. Skills and knowledge-related metrics did not differ significantly between the groups with the exception of the “knowledge of instruments” variable.ConclusionsOur data suggests that remote ultrasound-guided procedure training, although logistically complex, is a viable alternative to traditional in-person learning techniques even for a notoriously hands on skill like ultrasound guided knee arthrocentesis. Novice first-year medical student operators in the remote-training group were able to significantly increase their confidence and demonstrate competency in a manner statistically indistinguishable from those trained in-person. These results support the pedagogical validity of using remote training to teach ultrasound guided procedures which could have implications in rural and global health initiatives where educational resources are more limited.

Emotional understanding: a training study with Pakistani preschoolers

ABSTRACT This study was conducted to investigate whether a translated and adapted conversational training programme enhances emotion understanding in preschool children. Given the lack of such research in Pakistan (and outside the West in general), the training study reported here was conceived to fill this gap. Thirty-nine 5- to 6-year-olds at two school sites were randomly assigned to training and control groups, after screening for emotional and behavioural problems. The participants were pre- and post-tested on Emotional Comprehension after an 8-week intervention. Children from both groups listened to the translated and adapted version of the ‘Stories of Jack and Theo’, rich in mental state language. Children from the training group participated in conversations about the mental states of the story characters and themselves using semi-structured conversational procedures, while children from the control group were engaged in board games mediated by an adult. The findings showed that children in the training group outperformed those in the control group on emotional comprehension even after statistically controlling for earlier executive function and working memory.

The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (

ObjectiveDevelop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm.MethodsA total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed.ResultsThree radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830–0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%.ConclusionThe combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.

Interpretable Machine Learning Models for Predicting Critical Outcomes in Patients with Suspected Urinary Tract Infection with Positive Urine Culture.

(1) Background: Urinary tract infection (UTI) is a leading cause of emergency department visits and hospital admissions. Despite many studies identifying UTI-related risk factors for bacteremia or sepsis, a significant gap remains in developing predictive models for in-hospital mortality or the necessity for emergent intensive care unit admission in the emergency department. This study aimed to construct interpretable machine learning models capable of identifying patients at high risk for critical outcomes. (2) Methods: This was a retrospective study of adult patients with urinary tract infection (UTI), extracted from the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database. The critical outcome is defined as either in-hospital mortality or transfer to an intensive care unit within 12 h. ED visits were randomly partitioned into a 70%/30% split for training and validation. The extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms were constructed using variables selected from the stepwise logistic regression model. The XGBoost model was then compared to the traditional model and clinical decision rules (CDRs) on the validation data using the area under the curve (AUC). (3) Results: There were 3622 visits among 3235 unique patients diagnosed with UTI. Of the 2535 patients in the training group, 836 (33%) experienced critical outcomes, and of the 1087 patients in the validation group, 358 (32.9%) did. The AUCs for different machine learning models were as follows: XGBoost, 0.833; RF, 0.814; and SVM, 0.799. The XGBoost model performed better than others. (4) Conclusions: Machine learning models outperformed existing traditional CDRs for predicting critical outcomes of ED patients with UTI. Future research should prospectively evaluate the effectiveness of this approach and integrate it into clinical practice.

Evaluation of a new scoring system for assessing nerve invasion in resected pancreatic cancer: A single-center retrospective analysis

Nerve invasion (NI) is a characteristic feature of pancreatic cancer. Traditional dichotomous statements on the presence of NI are unreasonable because almost all cases exhibit NI when sufficient pathological sections are examined. The critical implications of NI in pancreatic cancer highlight the need for a more effective criterion. This study included 511 patients, who were categorized into a training group and a testing group at a ratio of 7:3. According to the traditional definition, NI was observed in 91.2 % of patients using five pathological slides in our study. The prevalence of NI increased as more pathological slides were used. The criterion of ‘two points of intraneural (endoneural) invasion in the case of four pathological slides’ has the highest receiver operating characteristic (ROC) score. Based on this new criterion, NI was proved to be an independent prognostic factor for overall survival (OS) and disease-free survival (DFS) and was also correlated with tumor recurrence (P = 0.004). Interestingly, gemcitabine-based chemotherapy regimen is an independent favorable factor for patients with high NI. In the high NI group, patients who received a gemcitabine-based regimen exhibited a better prognosis than those who did not receive the gemcitabine-based regimen for OS (P = 0.000) and DFS (P = 0.001). In conclusion, this study establishes assessment criteria to evaluate the severity of NI in order to predict patient outcomes.

Classification of expansive soils among problematic soils in the brazilian semiarid from artificial neural networks

The climatological and geomorphological conditions of the Brazilian semiarid region favor the formation of potentially expansive soils. The development of neural networks to identify and classify expansive soils in Pernambuco, and the need to export to soils across the semiarid region of Brazil is investigated in this study using a Multi-Layer Perceptron (backpropagation). The neural network was developed using 87 experimental data points, divided into three groups. The Training Group, consisting of 53 samples, using data inputs: sand percentage, clay percentage, plasticity index, activity index, and geological, pedological, and climatological classification. Selection Group, made up of 17 samples, was used to select the best network architecture, genetics algorithms formed of 7 inputs and 3 hidden nodes. The Test Group used 17 samples to evaluate the predictive accuracy of the expansion potential, obtaining an accuracy rate of 88,2%. This network was validated by applying it to 67 samples of problematic soils of the collapsible, expansive, and soft type from the Brazilian semiarid region, reaching an accuracy of 76,1%. Probabilistic Neural Networks were found to be efficient in evaluating the behavior of expansive soils, with the ability to deal with the absence of sample input data, demonstrating the ability to capture movement trends in the expansion of the soil surface, indicating the functions that introduce the effects of the composition potential on the expansion behavior, and determining the limit values of each of the input variables for the samples from the database used.

Whole-brain mechanism of neurofeedback therapy: predictive modeling of neurofeedback outcomes on repetitive negative thinking in depression

Real-time fMRI neurofeedback (rtfMRI-NF) has emerged as a promising intervention for psychiatric disorders, yet its clinical efficacy remains underexplored due to an incomplete mechanistic understanding. This study aimed to delineate the whole-brain mechanisms underpinning the effects of rtfMRI-NF on repetitive negative thinking in depression. In a double-blind randomized controlled trial, forty-three depressed individuals underwent NF training targeting the functional connectivity (FC) between the posterior cingulate cortex and the right temporoparietal junction, linked to rumination severity. Participants were randomly assigned to active or sham groups, with the sham group receiving synthesized feedback mimicking real NF signal patterns. The active group demonstrated a significant reduction in brooding rumination scores (d = −1.52, p < 0.001), whereas the sham group did not (d = −0.23, p = 0.503). While the target FC did not show discernible training effects or group differences, connectome-based predictive modeling (CPM) analysis revealed that the interaction between brain activity during regulation and brain response to the feedback signal was the critical factor in explaining treatment outcomes. The model incorporating this interaction successfully predicted rumination changes across both groups. The FCs significantly contributing to the prediction were distributed across brain regions, notably the frontal control, salience network, and subcortical reward processing areas. These results underscore the importance of considering the interplay between brain regulation activities and brain response to the feedback signal in understanding the therapeutic mechanisms of rtfMRI-NF. The study affirms rtfMRI-NF’s potential as a therapeutic intervention for repetitive negative thinking and highlights the need for a nuanced understanding of the whole-brain mechanisms contributing to its efficacy.

Nomogram predicting early urinary incontinence after radical prostatectomy

PurposeOne of the most frequent side effects of radical prostatectomy (RP) is urinary incontinence. The primary cause of urine incontinence is usually thought to be impaired urethral sphincter function; nevertheless, the pathophysiology and recovery process of urine incontinence remains unclear. This study aimed to identify potential risk variables, build a risk prediction tool that considers preoperative urodynamic findings, and direct doctors to take necessary action to reduce the likelihood of developing early urinary incontinence.MethodsWe retrospectively screened patients who underwent radical prostatectomy between January 1, 2020 and December 31, 2023 at the First People ‘s Hospital of Nantong, China. According to nomogram results, patients who developed incontinence within three months were classified as having early incontinence. The training group’s general characteristics were first screened using univariate logistic analysis, and the LASSO method was applied for the best prediction. Multivariate logistic regression analysis was carried out to determine independent risk factors for early postoperative urine incontinence in the training group and to create nomograms that predict the likelihood of developing early urinary incontinence. The model was internally validated by computing the performance of the validation cohort. The nomogram discrimination, correction, and clinical usefulness were assessed using the c-index, receiver operating characteristic curve, correction plot, and clinical decision curve.ResultsThe study involved 142 patients in all. Multivariate logistic regression analysis following RP found seven independent risk variables for early urinary incontinence. A nomogram was constructed based on these independent risk factors. The training and validation groups’ c-indices showed that the model had high accuracy and stability. The calibration curve demonstrates that the corrective effect of the training and verification groups is perfect, and the area under the receiver operating characteristic curve indicates great identification capacity. Using a nomogram, the clinical net benefit was maximised within a probability threshold of 0.01–1, according to decision curve analysis (DCA).ConclusionThe nomogram model created in this study can offer a clear, personalised analysis of the risk of early urine incontinence following RP. It is highly discriminatory and accurate, and it can help create efficient preventative measures and identify high-risk populations.

Vascular endothelial function and its response to moderate-intensity aerobic exercise in trained and untrained healthy young men

30 min of moderate-intensity aerobic exercise per day is recommended, but the response and adaptation of endothelial function (EF) to this exercise remains controversial. The purpose of this study was to determine the changes in EF in endurance trained and untrained individuals before and after this exercise and to compare the differences between trained and untrained individuals. Twelve endurance-trained male college athletes (trained group) and 12 untrained male college students (untrained group) performed a 30-min run at an intensity of 60% VO2max. Brachial artery flow-mediated dilation (FMD) was measured before exercise, 30 min and 60 min after exercise, and the following morning. Resting diameter and maximum diameter showed large time effects (p < 0.001, η2 = 0.533; p < 0.001, η2 = 0.502). Resting diameters at 30 and 60 min after exercise were higher than before exercise in both the untrained and trained groups (p < 0.05), and maximum diameters at 30 min after exercise were higher than before exercise in both the untrained and trained groups (p < 0.01). Resting diameter and maximum diameter also exhibited some group effects (p = 0.055, η2 = 0.157; p = 0.041, η2 = 0.176). Resting diameters and maximum diameters were higher in the trained group than in the untrained group before exercise (p < 0.05). FMD (%) showed no time, group, or time-group interaction effects. 30 min of moderate-intensity aerobic exercise can increase resting and maximal arterial diameters in both trained and untrained young men, but has no effect on FMD. Long-term endurance training has the potential to increase resting and maximal arterial diameters in young men, but not necessarily FMD.

Effects of visual and auditory cognitive tasks on postural balance in adolescents with intellectual disability: A comparative analysis of trained versus non-trained individuals

This study aimed to investigate the influence of visual and auditory cognitive tasks on postural balance in adolescents with intellectual disabilities (ID). Participants included two groups: a trained group (n = 11) with experience in athletic activities and a sedentary group (n = 14). The experiment used a comparative cross-sectional design, with data collection involving single-task (ST) and Visual dual-task (VDT), and auditory dual-task (ADT) conditions in both firm and foam surface conditions. Cognitive tests included the Working Memory Test (WMT) and the Selective Attention Test (SAT). Results revealed that the trained group demonstrated significantly superior balance performance (p < 0.05). During the SAT, VDT conditions had lower center of pressure (CoP) values than ADT conditions in the sedentary group (p < 0.01), this result was observed in the training group only in the WMT, suggesting greater postural instability during ADT. These findings highlight the complex relationship between cognitive function and motor control in adolescents with ID, highlighting the potential benefits of regular physical activity interventions to improve postural balance abilities in this population.

Analysis of combinatory effects of free weight resistance training and a high-protein diet on body composition and strength capacity in postmenopausal women - A 12-week randomized controlled trial

BackgroundMenopause has a significant impact on the endocrine system of middle-aged women, resulting in a loss of skeletal muscle mass (SMM), changes in fat mass (FM) and a reduction in strength capacity. Resistance training (RT) and a high-protein diet (HPD) are effective methods for maintaining or increasing SMM. This study aims to determine the effects of HPD and RT on body composition, muscle thickness and strength capacity in postmenopausal women. MethodsIn total 55 healthy postmenopausal women (age: 58.2 ± 5.6 years, weight 69.1 ± 9.6 kg, height 166.5 ± 6.5 cm) successfully participated in the study. The women were randomly assigned to either group: training + protein (2.5 g/kg fat-free mass (FFM)) (n = 15; TP); only training (n = 12; T); only protein (2.5 g/kg FFM) (n = 14; CP) or control (n = 14; C). TP and T performed RT for 12 weeks with three training sessions and five exercises each. CP and C were prohibited from training during the period. The main parameters analysed for body composition were FFM, SMM, FM, muscle thickness of the M. rectus femoris, M. biceps femoris, M. triceps brachii and M. biceps brachii muscles. Strength was tested using a dynamometer for grip strength and 1-RM in the squat (BBS) and deadlift (DL). ResultsThe SMM significantly increased by RT (TP: (Δ+1.4 ± 0.9 kg; p < 0.05; d = 0.4; T: Δ+1.2 ± 1.3kg; p < 0.05; d = 0.3) and FM could be reduced only in T: (Δ−2.4 ± 2.9 kg; p < 0.05; d = 0.3). In muscle thickness a significant increase in the M. biceps brachii in both training groups (TP: (Δ+0.4 ± 0.3 cm; p < 0.05; d = 1.6; T: (Δ+0.3 ± 0.3 cm; p < 0.05; d = 0.9) and in M. biceps femoris only in TP (Δ+0.3 ± 0.4 cm; p < 0.05; d = 0.9) were observed. HPD without training does not affect body composition, A significant increase in grip strength (TP: Δ+4.7 ± 2.4 kg; (p < 0.05; d = 1.5; T: (Δ+3.6 ± 3.0 kg; p < 0.05; d = 0.8), in BBS (TP: (Δ+30.0 ± 14.2 kg; p < 0.05; d = 1.5; T: (Δ+34.0 ± 12.0 kg; p < 0.05; d = 2.4) and in DL (TP: (Δ+20.8 ± 10.3 kg; p < 0.05; d = 1.6; T: (Δ+22.1 ± 7.6 kg; p < 0.05; d = 2.0) was observed in both training groups. The CP also recorded a significant increase in the BBS (Δ+7.5 ± 5.4 kg; p < 0.05; d = 0.4) and in DL (Δ+5.5 ± 7.7 kg; p < 0.05; d = 0.5). No significant differences were detected for TP and T for any of the parameters. ConclusionThe results indicate that RT enhances body composition and strength capacity in postmenopausal women and is a preventive strategy against muscle atrophy. Besides HPD without training has a trivial significant effect on BBS and DL. HPD with RT has no clear additive effect on body composition and strength capacity. Further studies are needed to confirm these observations.

Diagnostic value of Gd-EOB-DTPA-enhanced MRI radiomics models for dual-phenotype hepatocellular carcinoma

Objective: To develop and validate clinical and radiomics models based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI of dual-phenotype hepatocellular carcinoma (DPHCC) for preoperative differential diagnosis. Methods: Two hundred and fifty inpatients of hepatocellular carcinoma (HCC) confirmed by postoperative pathology, who underwent Gd-EOB-DTPA-enhanced MRI were retrospectively included. A total of 172 inpatients (72 DPHCC and 100 non-DPHCC) were included in Institution 1 (the First Affiliated Hospital of Soochow University) as a training cohort (between January 2020 and July 2023) and 78 inpatients (44 DPHCC and 34 non-DPHCC) were included in Institution 2 (the Third People's Hospital of Nantong) as an external validation cohort(between January 2019 and July 2023). The regions of interest of the tumor were delineated layer by layer in noncontrast phase, arterial phase (AP), portal venous phase (PP) and hepatobiliary phase (HBP) images. The software of FAE was used to extract the radiomics features of the images. Pearson correlation analysis and recursive feature elimination were used for feature selection. Each phase and combined radiomics models were established using logistic regression, linear discriminant analysis and support vector machine. Receiver operating characteristic curve and the areas under the curve (AUC) were used to evaluate and select the dominant radiomics model. The dominant radiomics model was combined with clinically independent predictors to construct a clinical radiomics model. Delong test was used to compare the performance of the models. Results: The age of the training cohort was (59.6±10.4) years, in which there were 135 men (78.5%). In the external validation cohort, the age was (57.8±9.2) years, including 56 men (71.8%). The maximum diameters of the lesions [M (Q1, Q3), 4.7 (2.6, 7.5) vs 2.7 (1.8, 4.4) cm, P<0.001] and the proportion of the multiple lesions (39.5% vs 16.7%, P<0.001) in the training cohort were higher than those in the external validation cohort. In the training group, the proportion of patients with hepatitis B virus (HBV) infection in the DPHCC subgroup (66.7%,48/172) was higher than that in non-DPHCC subgroup (49.0%,49/78,P=0.021). In the external validation cohort, the AUC (95%CI) of the PP [0.835 (0.733-0.937)] and combined radiomics models [0.786 (0.681-0.891)] were significantly higher than that of noncontrast phase [0.451 (0.319-0.584)], AP [0.566 (0.435-0.696)] and HBP models [0.496 (0.363-0.629)] (all P<0.05). There was no significant difference in AUC between PP radiomics model and combined radiomics model (P=0.189). The AUC between the radiomics models and clinical-radiomics models, which were brought into clinically independent variable HBV, showed no significant difference (all P>0.05). Conclusion: Gd-EOB-DTPA-enhanced MRI radiomics model based on portal venous phase may be available for discriminating DPHCC from non-DPHCC before operation.

Predicting cerebellar mutism syndrome in children using lesion map combined with clinical features.

This study aimed to develop a predictive model for cerebellar mutism syndrome (CMS) in pediatric patients with posterior fossa tumors, integrating lesion-symptom mapping (LSM) data with clinical factors, and to assess the model's performance. A cohort of pediatric patients diagnosed with posterior fossa tumors and undergoing surgery at Beijing Children's Hospital from July 2013 to December 2023 was analyzed. Clinical variables gender, age at surgery, tumor characteristics, hydrocephalus, surgical route and pathology were collected. LSM was used to link tumor locations with CMS outcomes. Lasso regression and logistic regression were employed for feature selection and model construction, respectively. Model performance was assessed using area under the curve (AUC) and accuracy metrics. The study included 197 patients in total, with CMS rates consistent across training, validation, and prospective groups. Significant associations were found between CMS and gender, tumor type, hydrocephalus, paraventricular edema, surgical route, and pathology. A predictive model combining voxel location data from LSM with clinical factors achieved high predictive performance (C-index: training 0.956, validation 0.933, prospective 0.892). Gender, pathology, and voxel location were identified as key predictors for CMS. The study established an effective predictive model for CMS in pediatric posterior fossa tumor patients, leveraging LSM data and clinical factors. The model's accuracy and robustness suggest its potential utility in clinical practice for early CMS risk assessment and intervention planning.

Synthetic lumbar MRI can aid in diagnosis and treatment strategies based on self-pix networks

CT and MR tools are commonly used to diagnose lumbar fractures (LF). However, numerous limitations have been found in practice. The aims of this study were to innovate and develop a spinal disease-specific neural network and to evaluate whether synthetic MRI of the LF affected clinical diagnosis and treatment strategies. A total of 675 LF patients who met the inclusion and exclusion criteria were included in the study. For each participant, two mid-sagittal CT and T2-weighted MR images were selected; 1350 pairs of LF images were also included. A new Self-pix based on Pix2pix and Self-Attention was constructed. A total of 1350 pairs of CT and MR images, which were randomly divided into a training group (1147 pairs) and a test group (203 pairs), were fed into Pix2pix and Self-pix. The quantitative evaluation included PSNR and SSIM (PSNR1 and SSIM1: real MR images and Pix2pix-generated MR images; PSNR2 and SSIM2: real MR images and Self-pix-generated MR images). The qualitative evaluation, including accurate diagnosis of acute fractures and accurate selection of treatment strategies based on Self-pix-generated MRI, was performed by three spine surgeons. In the LF group, PSNR1 and PSNR2 were 10.884 and 11.021 (p < 0.001), and SSIM1 and SSIM2 were 0.766 and 0.771 (p < 0.001), respectively. In the ROI group, PSNR1 and PSNR2 were 12.350 and 12.670 (p = 0.004), and SSIM1 and SSIM2 were 0.816 and 0.832 (p = 0.005), respectively. According to the qualitative evaluation, Self-pix-generated MRI showed no significant difference from real MRI in identifying acute fractures (p = 0.689), with a good sensitivity of 84.36% and specificity of 96.65%. No difference in treatment strategy was found between the Self-pix-generated MRI group and the real MRI group (p = 0.135). In this study, a disease-specific GAN named Self-pix was developed, which demonstrated better image generation performance compared to traditional GAN. The spine surgeon could accurately diagnose LF and select treatment strategies based on Self-pix-generated T2 MR images.

Euterpe Oleracea Martius (Açaí) Extract and Resistance Exercise Modulate Cardiac Parameters of Hypertensive Rats.

The study evaluated the effects of resistance exercise training and açaí supplementation on cardiac parameters in hypertensive animals. For this study, rats from the Wistar and SHR lines (spontaneously hypertensive rats) were used. The animals were divided into 5 groups: Wistar Control (C); Control Hypertensive (H); Trained Hypertensive (HT); Hypertensive and Supplemented with Açaí (HA); and Hypertensive Trained and Supplemented with Açaí (HAT). Resistance exercise training was carried out through climbing. The supplemented groups received 3 g of açaí/kg of body mass. The animals' systolic blood pressure (SBP), body mass, and physical test were measured at the beginning and end of the intervention. At the end, an echocardiographic analysis was performed. Histological analysis and oxidative stress of the LV were performed. It was found that hypertensive animals showed an increase in SBP, and the treatments reduced this parameter. The trained groups achieved higher values of maximum carrying load. Hypertension increased the dimension of the left ventricular free wall in diastole and reduced ejection and shortening fractions. The trained groups showed improvement in ejection and shortening fractions. The H group increased the proportion of extracellular matrix and reduced the proportion of cells, with the HAT group attenuating this change. Cell diameter was greater in group H, and all treatments reduced this parameter. Hypertension increased the concentration of malondialdehyde and decreased catalase activity in LV. The treatments managed to mitigate this damage. It is concluded that the treatments managed to generate positive cardiovascular adaptations, and their combination enhanced these effects.

Different intensities of aerobic training for patients with type 2 diabetes mellitus and knee osteoarthritis: a randomized controlled trial.

The purpose of this study was to compare different intensities of aerobic exercise for patients with knee osteoarthritis (KOA) and type 2 diabetes mellitus (T2DM) in terms of glycemic control, pain relief, and functional outcomes. A prospective randomized open-label parallel multicenter clinical trial conducted at two hospitals in Shanghai and Sichuan that included 228 patients with type 2 diabetes mellitus (T2DM) and knee osteoarthritis (KOA). Enrollment occurred between January 2021 and February 2023, and follow-up was completed in September 2023. Participants were randomized to threshold training/high-intensive stationary cycling training (n=76), intensive endurance/moderate-intensive stationary cycling training (n=77), and regular rehabilitation programs (n=75). The primary outcome at the 6-month follow-up was the HbA1c level. Key secondary outcomes included the Knee Injury and Osteoarthritis Outcome Score (KOOS) subscale of pain and quality of life. Of 228 patients, 212 (93%) completed the trial. The mean adjusted (sex, baseline BMI, and baseline outcome measures) HbA1c level at the 6-month follow-up decreased significantly in the high-intensive training group compared with other groups (high-intensity group vs. control group; difference, 0.51%, 95% confidence interval, 0.05% to 1.15%). Mean KOOS subscales of pain and quality of life were statistically significantly different between the control group and moderate-intensity or high-intensity groups, but no statistical differences were noted between the different intensities of aerobic exercise. Patients in all groups achieved a greater reduction in BMI but no significant differences were observed between groups. In KOA and T2DM patients, high-intensity stationary cycling can significantly improve glycemic control compared with moderate-intensity and regular rehabilitation programs. However, high-intensity stationary cycling does not exert a superior effect on pain relief and functional improvement for KOA compared with moderate-intensity and regular rehabilitation programs.

Effects of In-Season Strength Training on Physical Fitness and Injury Prevention in North African Elite Young Female Soccer Players

BackgroundStrength training (ST) primarily enhances physical fitness (e.g., muscle strength, power, speed) and bone density in female soccer players. Less information is available on the injury preventive effects of ST in female athletes. Accordingly, this study aimed to investigate the effects of a 12-week in-season ST on measures of physical fitness and injury occurrence in young elite female soccer players.MethodsThirty elite female soccer players (15.4 ± 1.9 years; maturity offset + 2.3 ± 1.1 years) participated in this study, and were randomly assigned to a strength training group (STG, n = 14) or an active control (CG, n = 16) group. ST lasted twelve weeks and included full body muscle strengthening exercises using primarily weight machines at progressive intensities ranging between 40 and 85% of the one-repetition-maximum (1-RM). The CG practiced a traditional soccer training program. Overall, training volumes of the two groups were similar with a training load (rating of perceived exertion × time) of 1158.4 ± 67.7 arbitrary unity (AU) for the STG and 1188.8 ± 44.1 AU for the CG. Pre and post training, the following physical fitness tests were applied: dynamic muscle strength (relative [to body mass] and absolute 1-RM bench/leg press, lat-pull down), jump performance (countermovement jump [CMJ], squat jump [SJ], five-jump-test [5JT]), linear-sprint speed (5-m, 10-m, 30-m), change-of-direction speed (T-test with and without ball), sport-specific performance (Yo-Yo Intermittent Level1 [YYIRTL1], and repeated shuttle sprint ability [RSSA]). The injury rate per 1000-h exposure was monitored throughout the soccer season.ResultsNo significant baseline differences were observed between groups. Statistically significant group-by-time interactions were found for absolute (p < 0.001, d = 2.59) and relative 1-RM bench press (p < 0.001, d = 2.39), absolute 1-RM lat-pull down (p < 0.001, d = 1.68), and relative 1-RM leg press (p < 0.001, d = 1.72). Significant group-by-time interactions were observed for CMJ (p = 0.005, d = 1.27), RSSAmean (p = 0.007, d = 0.81), and RSSAtotal (p < 0.001, d = 1.90). Post-hoc tests indicated that the STG group demonstrated greater improvements in all tested variables compared to CG (1.2 < d < 2.5). However, no significant interaction effects were noted for measures of linear sprint speed and YYIRTL1 performance. Additionally, non-contact injuries during the season were significantly lower (p = 0.003, d = 1.31) in the STG (0.48/1000 h of exposure) than the CG (2.62/1000 h of exposure).ConclusionsTwelve weeks of an in-season ST resulted in larger physical fitness improvements and fewer injuries compared with an active control in elite young female soccer players. Accordingly, ST should be systematically applied in female soccer to enhance performance and prevent injuries.

Clinical data and quantitative CT parameters combined with machine learning to predict short-term prognosis of severe COVID-19 in the elderly

Rationale and objectivesThis study aims to evaluate the effectiveness of integrating clinical data and quantitative CT parameters with machine learning techniques in forecasting the short-term outcomes of severe COVID-19 in elderly patients. Materials and methodsIn this retrospective study, we analyzed the clinical profiles and chest quantitative CT parameters of 239 elderly patients with severe COVID-19 admitted for treatment. The cohort included 61 deceased patients (death group) and 178 who recovered and were discharged (survival group). The participants were randomly assigned into a training group (n = 167) and a validation group (n = 72). Quantitative CT parameters were measured using the 3D-Slicer software. Univariate and multivariate logistic regression analyses identified independent risk factors for mortality. Predictive models were developed employing four machine learning algorithms: Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). ResultsBoth univariate and multivariate logistic regression analyses revealed age, hypersensitive C-reactive protein (hs-CRP), and solid organ volume percentage (SOV%) as independent predictors of mortality. The Area Under the Curve (AUC) values for the LR, RF, DT, and SVM models in the training group were 0.795, 0.726, 0.854, and 0.589, respectively; for the validation group, they were 0.817, 0.634, 0.869, and 0.754, respectively. The DT algorithm outperformed other models in both the training and validation groups, emerging as the most effective predictive model in this study. ConclusionThe combination of clinical data and quantitative CT parameters with machine learning approaches is highly valuable in predicting the short-term prognosis of severe COVID-19 in the elderly. Among the various models tested, the Decision Tree algorithm-based model proved to be the most accurate and reliable in this context.

Development and validation of a nomogram to predict overall survival of gastroenteropancreatic neuroendocrine carcinoma: a SEER database analysis.

Gastroenteropancreatic neuroendocrine carcinoma (GEP-NEC) is a rare group of diseases with poor prognosis and the assessment of its prognosis is a significant challenge. This study aimed to develop and validate a prognostic nomogram to assess overall survival (OS) in patients with GEP-NEC. Patients diagnosed with poorly differentiated GEP-NEC were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2011 and 2015 and were randomly assigned to the training or validation cohort in a 7:3 ratio. The data included details of clinicopathological characteristics, therapeutic interventions and survival outcomes. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. Nomogram was used to predict OS at 1 and 2 years. The nomogram was internally validated with validation cohort, and its predictive ability was evaluated using concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and integrated discrimination improvement (IDI) index. A total of 887 patients were divided into the training group (n=623) and the validation group (n=264). A total of 476 patients (53.66%) were in stage IV. Based on multivariate analysis, a nomogram was constructed with age, gender, N stage, tumor size, primary tumor resection, radiotherapy and chemotherapy (P<0.05). The C-index was 0.701 [95% confidential interval (CI): 0.677-0.725] and 0.731 (95% CI: 0.698-0.764) for the training and validation groups, respectively. The C-index, ROC, IDI and DCA results indicated that this nomogram model has a good predictive value. In this study, a nomogram model based on seven independent prognostic factors provided visualization of the risk and could help clinicians predict the 1-year and 2-year OS for GEP-NEC. This tool can provide personalized survival predictions and improve clinical decision making for the management of GEP-NEC.

The role of coagulopathy and subdural hematoma thickness at admission in predicting the prognoses of patients with severe traumatic brain injury: a multicenter retrospective cohort study from China.

Traumatic brain injury (TBI) is one of the diseases with high disability and mortality worldwide. Recent studies have shown that TBI-related factors may change the complex balance between bleeding and thrombosis, leading to coagulation disorders. The aim of this retrospective study was to investigate the prediction of coagulopathy and subdural hematoma thickness at admission using the Glasgow Outcome Scale (GOS) in patients with severe TBI at 6 months after discharge. In this retrospective cohort study, a total of 1006 patients with severe TBI in large medical centers in three different provinces of China from June 2015 to June 2021 were enrolled after the exclusion criteria, and 800 patients who met the enrollment criteria were included. A receiver operating characteristic (ROC) curve was used to determine the best cut-off values of platelet (PLT), international normalized ratio (INR), activated partial thromboplastin time (APTT), and subdural hematoma (SDH) thickness. The ROC curve, nomogram, calibration curve, and the decision curve were used to evaluate the predictive effect of the coagulopathy and Coagulopathy-SDH(X1) models on the prognoses of patients with severe TBI, and the importance of predictive indicators was ranked by machine learning. Among the patients with severe TBI on admission, 576/800 (72%) had coagulopathy, 494/800 (61%) had SDH thickness ≥14.05mm, and 385/800 (48%) had coagulopathy combined with SDH thickness ≥14.05mm. Multivariate logistic regression analyses showed that age, pupil, brain herniation, WBC, CRP, SDH, coagulopathy, and X1 were independent prognostic factors for GOS after severe TBI. Compared with other single indicators, X1 as a predictor of the prognosis of severe TBI was more accurate. The GOS of patients with coagulopathy and thick SDH (X1, 1 point) at 6 months after discharge was significantly worse than that of patients with coagulopathy and thin SDH (X1, 2 points), patients without coagulopathy and thick SDH (X1, 3 point), and patients without coagulopathy and thin SDH (X1, 4 points). In the training group, the C-index based on the coagulopathy nomogram was 0.900. The C-index of the X1-based nomogram was 0.912. In the validation group, the C-index based on the coagulopathy nomogram was 0.858. The C-index of the X1-based nomogram was 0.877. Decision curve analysis also confirmed that the X1-based model had a higher clinical net benefit of GOS at 6 months after discharge than the coagulopathy-based model in most cases, both in the training and validation groups. In addition, compared with the calibration curve based on the coagulopathy model, the prediction of the X1 model-based calibration curve for the probability of GOS at 6 months after discharge showed better agreement with actual observations. Machine learning compared the importance of each independent influencing factor in the evaluation of GOS prediction after TBI, with results showing that the importance of X1 was better than that of coagulopathy alone. Coagulopathy combined with SDH thickness could be used as a new, accurate, and objective clinical predictor, and X1, based on combining coagulopathy with SDH thickness could be used to improve the accuracy of GOS prediction in patients with TBI, 6 months after discharge.