Targeted Development and Validation of Clinical Prediction Models in Secondary Care Settings: Opportunities and Challenges for Electronic Health Record Data.

Unlabelled: Before deploying a clinical prediction model (CPM) in clinical practice, its performance needs to be demonstrated in the population of intended use. This is also called "targeted validation." Many CPMs developed in tertiary settings may be most useful in secondary care, where the patient case mix is broad and practitioners need to triage patients efficiently. However, since structured or rich datasets of sufficient quality from secondary to assess the performance of a CPM are scarce, a validation gap exists that hampers the implementation of CPMs in secondary care settings. In this viewpoint, we highlight the importance of targeted validation and the use of CPMs in secondary care settings and discuss the potential and challenges of using electronic health record (EHR) data to overcome the existing validation gap. The introduction of software applications for text mining of EHRs allows the generation of structured "big" datasets, but the imperfection of EHRs as a research database requires careful validation of data quality. When using EHR data for the development and validation of CPMs, in addition to widely accepted checklists, we propose considering three additional practical steps: (1) involve a local EHR expert (clinician or nurse) in the data extraction process, (2) perform validity checks on the generated datasets, and (3) provide metadata on how variables were constructed from EHRs. These steps help to generate EHR datasets that are statistically powerful, of sufficient quality and replicable, and enable targeted development and validation of CPMs in secondary care settings. This approach can fill a major gap in prediction modeling research and appropriately advance CPMs into clinical practice.

Unveiling the silent suffering: Examining the complexities of disclosure and concealment strategies in women living with obstetric fistula.

Obstetric Fistula (OF), one of the tragic birth injuries in developing countries, overwhelms women living with this condition with multifaceted effects. Although concealing has been justified in some studies, revealing is also encouraged. Such uncertainty leaves women in a loop of tension in managing the disclosure or non-disclosure of their diagnosis. They resort to several strategies to manage their private information. Using Communication Privacy Management (CPM) Theory, this study documents the disclosure or non-disclosure strategies that women living with OF adopt to manage their diagnosis information. Data was derived from semi-structured interviews conducted with 19 women either living with OF presently or have lived with it before. The thematic analysis yielded categories of who, when, what, and how. These categories were further subdivided into nine specific practices or strategies for revealing. However, the categories under the non-disclosure focused on only who and how, where four themes emerged. This study has both practical and theoretical significance by identifying disclosure and non-disclosure intervention strategies useful for providing relief for women diagnosed with OF.

Live-cell imaging under centrifugation characterized the cellular force for nuclear centration in the Caenorhabditis elegans embryo.

Organelles in cells are appropriately positioned, despite crowding in the cytoplasm. However, our understanding of the force required to move large organelles, such as the nucleus, inside the cytoplasm is limited, in part owing to a lack of accurate methods for measurement. We devised a method to apply forces to the nucleus of living Caenorhabditis elegans embryos to measure the force generated inside the cell. We used a centrifuge polarizing microscope to apply centrifugal force and orientation-independent differential interference contrast microscopy to characterize the mass density of the nucleus and cytoplasm. The cellular forces moving the nucleus toward the cell center increased linearly at ~12 pN/µm depending on the distance from the center. The frictional coefficient was ~980 pN s/µm. The measured values were smaller than the previously reported estimates for sea urchin embryos. The forces were consistent with the centrosome-organelle mutual pulling model for nuclear centration. The frictional coefficient was reduced when microtubules were shorter or detached from nuclei in mutant embryos, demonstrating the contribution of astral microtubules. Finally, the frictional coefficient was higher than a theoretical estimate, indicating the contribution of uncharacterized properties of the cytoplasm.

Connectome-based prediction modeling of cognitive control using functional and structural connectivity.

BACKGROUND: Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks. METHODS: The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control. RESULTS: Our results showed that both structural (r values 0.263-0.375) and functional (r values 0.336-0.503) connectomes can significantly predict individuals' cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns. CONCLUSIONS: The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.

Is predictability of the conditioning stimulus (CS) a critical factor in conditioned pain modulation (CPM)?

INTRODUCTION: Conditioned pain modulation (CPM) allows to investigate endogenous pain modulation and its clinical outcomes. Although co-activation of emotions has been shown to affect CPM, the impact of 'threat,' which may accompany CPM stimulation itself, has been mostly neglected. A critical factor for the threat level of the conditioning stimulus (CS) may be its predictability. METHODS: 38 healthy participants (18 female) took part in a CPM study with pressure stimulation on the leg (blood-pressure cuff) serving as CS and heat stimulation on the forearm (contact thermode; CHEPS) serving as test stimulus (TS). While CS varied in intensity and -as operationalisation of threat- in temporary predictability, TS was kept constant. CPM effects were studied by EEG parameters (N2P2) and pain ratings. RESULTS: We found a significant CPM effect when considering N2P2, with low CS predictability augmenting CPM inhibition; in contrast, a surprisingly facilitatory CPM effect occurred in pain ratings (in the high CS predictability condition). The threat manipulation was only partially successful because CS intensity increased the threat ratings but not -as intended- CS predictability. Correlations between subjective and psychophysiological CPM responses were low. DISCUSSION: The differing CPM effects in subjective and psychophysiological responses, with both inhibitory and facilitatory effects, is puzzling but has already been observed earlier. The consideration of the CPM stimulation as major threat that is emotionally active is theoretically clearly justifiable but the operationalisation by means of different levels of CS predictability as in the present study might not have been ideal. Thus, further attempts of experimental verification are warranted.

Connectome-based predictive modeling of brain pathology and cognition in Autosomal Dominant Alzheimer's Disease.

INTRODUCTION: Autosomal Dominant Alzheimer's Disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin-1 (PSEN1) E280A carriers' mutation has allowed us to observe incipient tau tangles accumulation as early as 6 years prior to symptom onset. METHODS: Resting-state functional Magnetic Resonance Imaging (fMRI) and Positron-Emission Tomography (PET) scans were acquired in a group of PSEN1 carriers (n=32) and non-carrier family members (n=35). We applied Connectome-based Predictive Modeling (CPM) to examine the relationship between the participant's functional connectome and their respective tau/amyloid-ß levels and cognitive scores (word list recall). RESULTS: CPM models strongly predicted tau concentrations and cognitive scores within the carrier group. The connectivity patterns between the temporal cortex, default mode network, and other memory networks were the most informative of tau burden. DISCUSSION: These results indicate that resting-state fMRI methods can complement PET methods in early detection and monitoring of disease progression in ADAD.

A critical review of the use of R2 in risk equalization research.

Nearly all empirical studies that estimate the coefficients of a risk equalization formula present the value of the statistical measure R2. The R2-value is often (implicitly) interpreted as a measure of the extent to which the risk equalization payments remove the regulation-induced predictable profits and losses on the insured, with a higher R2-value indicating a better performance. In many cases, however, we do not know whether a model with R2 = 0.30 reduces the predictable profits and losses more than a model with R2 = 0.20. In this paper we argue that in the context of risk equalization R2 is hard to interpret as a measure of selection incentives, can lead to wrong and misleading conclusions when used as a measure of selection incentives, and is therefore not useful for measuring selection incentives. The same is true for related statistical measures such as the Mean Absolute Prediction Error (MAPE), Cumming's Prediction Measure (CPM) and the Payment System Fit (PSF). There are some exceptions where the R2 can be useful. Our recommendation is to either present the R2 with a clear, valid, and relevant interpretation or not to present the R2. The same holds for the related statistical measures MAPE, CPM and PSF.

Efficacy and safety assessment of traditional Chinese patent medicine for dyslipidemia: a systematic review of randomized clinical trials with meta-analysis and trial sequential analysis.

Background: The overall prevalence of dyslipidemia continues to increase, which poses a significant risk for coronary artery disease. Some patients with dyslipidemia do not respond to or benefit from conventional lipid-lowering therapy, which warrants the need for alternative and complementary therapies. Chinese patent medicine (CPM) has shown great potential in the treatment of dyslipidemia, but its clinical value needs to be further explored. This study aims to systematically evaluate the efficacy and safety of CPM in treating dyslipidemia. Methods: This study was registered in INPLASY as INPLASY202330090. The randomized controlled trials included in this study were published in January 2013 to March 2023 and retrieved from the Web of Science, PubMed, Embase, Cochrane Library, SinoMed, China National Knowledge Internet, WanFang, and VIP. The bias risk in the study was independently evaluated by two reviewers using the Cochrane Randomized Trial Bias Risk Tool (RoB 2) Review Manager 5.4 software was used for the overall effect analysis and subgroup analysis of four blood lipids, and the trial sequential analysis (TSA) was conducted to check the results. Results: A total of 69 studies were included, involving 6,993 participants. The methodological quality was in the middle level. Meta-analysis showed that CPM markedly improved the levels of total cholesterol (TC) [mean difference (MD) =-0.54 mmol/L; 95% confidence interval (CI): -0.71 to -0.37; P<0.001], triglyceride (TG) (MD =-0.43 mmol/L; 95% CI: -0.53 to -0.33; P<0.001), low-density lipoprotein cholesterol (LDL-C) (MD =-0.40 mmol/L; 95% CI: -0.50 to -0.30; P<0.001) and increased levels of high-density lipoprotein cholesterol (HDL-C) (MD =0.23 mmol/L; 95% CI: 0.18 to 0.27; P<0.001), in patients with dyslipidemia. Though CPM did not differ significantly from statins when used alone, it could improve lipid profile better in all cases when used in combination with statins and with drugs used for comorbidities or co-morbidities. Subgroup analysis found that the efficacy of pill formulations was superior to other formulations, and CPM showed better lipid-lowering response in the context of comorbidity. The TSA confirmed the robustness of the analysis of the LDL-C level. No significant difference was observed in the incidence of adverse events between the treatment group and the control group [risk ratio (RR) =0.89; 95% CI: 0.69-1.16; P=0.40]. Conclusions: CPM can yield superior therapeutic effects in ameliorating dyslipidemia without exacerbating adverse effects as an alternative and complementary therapy. In addition, the therapeutic effect can be improved by emphasizing pill formulation and strengthening the standardization of syndromes.

Cross-sectional and longitudinal analysis of conditioned pain modulation and pain in fibromyalgia: CPM as an effect modifier of pain changes over time.

Background and purpose: Fibromyalgia (FM) is associated with altered descending pain modulatory pathways, which can be assessed through Conditioned Pain Modulation (CPM). In this study, we aimed to explore the relationship between CPM and self-reported baseline characteristics in patients with fibromyalgia. We also performed a longitudinal analysis exploring CPM as a potential predictor of clinical improvement over time in individuals with FM. Methods: We performed cross-sectional univariable and multivariable analyses of the relationship between CPM and other variables in 41 FM patients. We then performed longitudinal analyses, building linear mixed effects models with pain in the Visual Analogue Scale (VAS) as the dependent variable, and testing for the interaction between time and CPM. We also tested the interaction between CPM and time in models using other outcomes, such as the revised Fibromyalgia Impact Questionnaire (FIQR) and Quality of Life Scale (QOLs). Results: We found no association between CPM and other demographic and clinical variables in the univariable analysis. We found a statistically significant association in the multivariable linear regression model between CPM and the QOLs at baseline, after controlling for age, sex, and duration of symptoms. In the longitudinal analyses, we found that CPM is an effect modifier for clinical improvement over time for the pain VAS, QOLs and FIQR: individuals with low-efficient CPM at baseline have a different (improved) pattern of response over time when compared to those with high-efficient CPM. Conclusions: Our findings suggest that CPM is not a reliable biomarker of clinical manifestations in chronic pain patients during cross-sectional assessments. However, our results are consistent with previous findings that CPM can be used to predict the evolution of clinical pain over time. We expect that our findings will help in the selection of patients with the best profile to respond to specific interventions and assist clinicians in tailoring pain treatments.

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.

MAIN PROBLEM: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia. METHODS: A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients. RESULTS: CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method. CONCLUSIONS: This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.

Evaluation and obstacle analysis of sustainable development in small towns based on multi-source big data: A case study of 782 top small towns in China.

Evaluating the sustainable development level and obstacle factors of small towns is an important guarantee for implementing China's new-type urbanization and rural revitalization strategies, and is also a key path to promoting the United Nations Sustainable Development Goal 11 (SDG11). Traditional evaluation methods (such as Analytic Hierarchy Process, AHP, and Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) mainly calculate the comprehensive score of each indicator through weighting. These methods have limitations in handling multidimensional data and system nonlinearity, and they cannot fully reveal the complex relationships and interactions within the sustainability systems of small towns. In contrast, the evaluation model combining Principal Component Analysis (PCA) and Catastrophe Progression Method (CPM) used in this study can better handle multidimensional data and system nonlinear relationships, reducing subjectivity in evaluation and improving the accuracy and reliability of the assessment results. The specific research process is as follows: First, based on the United Nations SDG11 framework, using multi-source big data, a theoretical framework and evaluation index system for the sustainable development of small towns suitable for the Chinese context were established. The impact of county-level factors on the sustainable development of small towns was also considered, and an entropy weight-grey correlation model was used to measure these impacts, resulting in a town-level dataset incorporating county-level influences. Secondly, the sustainability levels of 782 top small towns in China were evaluated using the comprehensive evaluation model based on PCA-CPM Model. Finally, an improved diagnostic model was used to identify obstacles influencing the sustainable development of small towns. The main findings include: 52.69% of the small towns have a sustainable development score exceeding 0.7255, indicating that the overall performance of small towns is at a medium to high development level. The development of small towns exhibits significant differences across regions and types, which are closely linked to county-level effects. Economic and social factors are the main obstacles to the sustainable development of small towns, and the impact of these obstacles intensifies from the eastern to the central, western, and northeastern regions. This study provides valuable insights for policymakers and scholars, promoting a deeper understanding of the sustainable development of small towns.

A connectome-based model of delusion in schizophrenia using functional connectivity under working memory task.

Delusion is an important feature of schizophrenia, which may stem from cognitive biases. Working memory (WM) is the core foundation of cognition, closely related to delusion. However, the knowledge of neural mechanisms underlying the relationship between WM and delusion in schizophrenia is poorly investigated. Two hundred and thirty patients with schizophrenia (dataset 1: n = 130; dataset 2: n = 100) were enrolled and scanned for an N-back WM task. We constructed the WM-related whole-brain functional connectome and conducted Connectome-based Predictive Modelling (CPM) to detect the delusion-related networks and built the correlation model in dataset 1. The correlation between identified networks and delusion severity was tested in a separate, heterogeneous sample of dataset 2 that mainly includes early-onset schizophrenia. The identified delusion-related network has a strong correlation with delusion severity measured by the NO.20 item of SAPS in dataset 1 (r = 0.433, p = 2.7 × 10-7, permutation-p = 0.035), and can be validated in the same dataset by using another delusion measurement, that is, the P1 item of PANSS (r = 0.362, p = 0.0005). It can be validated in another independent dataset 2 (NO.20 item of SAPS for r = 0.31, p = 0.0024, P1 item of PANSS for r = 0.27, p = 0.0074). The delusion-related network comprises the connections between the default mode network (DMN), cingulo-opercular network (CON), salience network (SN), subcortical, sensory-somatomotor network (SMN), and visual networks. We successfully established correlation models of individualized delusion based on the WM-related functional connectome and showed a strong correlation between delusion severity and connections within the DMN, CON, SMN, and subcortical network.

Attention-guided context asymmetric fusion networks for the liver tumor segmentation of computed tomography images.

Background: Liver tumor segmentation based on medical imaging is playing an increasingly important role in liver tumor research and individualized therapeutic decision-making. However, it remains a challenging in terms of the accuracy of automatic segmentation of liver tumors. Therefore, we aimed to develop a novel deep neural network for improving the results from the automatic segmentation of liver tumors. Methods: This paper proposes the attention-guided context asymmetric fusion network (AGCAF-Net), combining attention guidance and fusion context modules on the basis of a residual neural network for the automatic segmentation of liver tumors. According to the attention-guided context block (AGCB), the feature map is first divided into multiple small blocks, the local correlation between features is calculated, and then the global nonlocal fusion module (GNFM) is used to obtain the global information between pixels. Additionally, the context pyramid module (CPM) and asymmetric semantic fusion module (AFM) are used to obtain multiscale features and resolve the feature mismatch during feature fusion, respectively. Finally, we used the liver tumor segmentation benchmark (LiTS) dataset to verify the efficiency of our designed network. Results: Our results showed that AGCAF-Net with AFM and CPM is effective in improving the accuracy of liver tumor segmentation, with the Dice coefficient increasing from 82.5% to 84.1%. The segmentation results of liver tumors by AGCAF-Net were superior to those of several state-of-the-art U-net methods, with a Dice coefficient of 84.1%, a sensitivity of 91.7%, and an average symmetric surface distance of 3.52. Conclusions: AGCAF-Net can obtain better matched and accurate segmentation in liver tumor segmentation, thus effectively improving the accuracy of liver tumor segmentation.

Assessment of a continuous passive motion assistive device in dogs following stifle surgery.

Canine rehabilitation optimizes recovery and the quality of life in dogs with musculoskeletal conditions or after surgery. Achieving proper range of motion (ROM) is vital post-stifle surgery, often accomplished through manual therapy and active exercises. We investigated the mechanical performance of a continuous passive motion (CPM) device for dogs and its potential use in canine rehabilitation therapy. In the ethical review process, our research was accepted to be evaluated in a sample of four dogs that had undergone left stifle surgery. Each dog underwent four sessions with the device at three different speeds. Results showed the device replicated extension angles close to goniometer measurements used in manual therapy. Flexion was also achieved, but not to the same extent. A force threshold stopped the device, avoiding discomfort in dogs with restricted ROM. Dog-specific factors like body position, opposition to movement, limb size, stage of recovery, haircoat, and discomfort, appeared to influence device operation. Mechanical improvements to allow for enhanced flexion are recommended in future CPM device designs, including a resistance threshold that could be adjusted for individual dogs and stages of healing. This study serves as a foundation for future advancements in canine rehabilitation systems. A canine CPM device may provide an affordable option to improve ROM. This could be beneficial for dog owners, who may not be comfortable with manual therapy, to assist with home rehabilitation exercises.

Postoperative Continuous Passive Motion Does Not Improve the Range of Movement Achieved After Manipulation Under Anesthetic for Stiffness in Total Knee Replacement.

Background: Stiffness is a common complication following total knee arthroplasty. Manipulation under anesthesia (MUA) is an intervention that can potentially improve range of motion (ROM). Continuous passive motion (CPM) therapy has been utilized to enhance post-MUA ROM, but its effectiveness remains debated. This study assesses whether CPM therapy after MUA results in superior ROM outcomes compared to MUA alone. Methods: A retrospective analysis included patients undergoing MUA for stiff primary total knee arthroplasty between 2017 and 2022. Demographics and ROM data were collected. Patients were in 2 groups: those who received inpatient CPM post-MUA and those who received day-case MUA alone. Complications and further interventions were noted. Results: Of 126 patients, 39 underwent MUA only (day-case group), and 87 received CPM and MUA (inpatient group). Mean preoperative ROM was 69.4° (standard deviation [SD]:18.0°) and 73.9° (SD: 18.1°) for inpatient and day-case groups, respectively. Mean post-MUA ROM improved by 39.4° (SD: 17.7°) and 25.5° (SD: 11.1°) inpatient groups and day-case, respectively. The mean percentage of ROM gained at MUA maintained at final follow-up was 63.7% (40.8%) and 67.0% (47.5%) inpatient and day-case groups, respectively. Conclusions: This study found no advantage in the routine use of CPM post-MUA for stiff total knee replacement patients, suggesting it may not provide sustained ROM improvements compared to MUA alone. Cost-effectiveness and patient selection merit further investigation.

Chickpea (Cicer arietinum L.) growth, nodulation, and yield as affected by varieties, Mesorhizobium strains, and NPSB fertilizer in Southern Ethiopia.

A significant legume crop in Ethiopia, chickpeas (Cicer arietinum L.) have several advantages, including high nutritional value and the capacity to improve soils deficient in nitrogen through biological nitrogen fixation using several endosymbiotic Mesorhizobium spp. strains. However, the host variety, the soil's capacity to hold nutrients, and the endosymbiont's innate physiological traits all affect how efficient the strains are. The primary obstacles to its cultivation in the research area are inadequate agronomic methods and low soil fertility [low nitrogen (N), low soil organic matter (OM), low accessible phosphorous (P), sulfur (S), and boron (B)], which results in ineffective nodulation. To evaluate the effects of NPSB fertilization and inoculation, a field experiment was carried out in Buchach Kebele's Cheha area during the primary cropping season of 2021/22. The trial included two chickpea kinds (Local and Arerti), two NPSB levels (zero and 121 kg NPSB ha-1), and four levels of Mesorhizobium strains (CP-M41, CP-EAL 029, CP-M20b, and un-inoculated control). A randomized complete block design (RCBD) was used to organize the treatments in a factorial form with three replications. In comparison to the single application and the control, the interaction impact of strains, NPSB fertilizer, and variety greatly increased nodulation parameters, growth parameters, yield, and yield components. The Arerti variety combined with the CP-M41 Mesorhizobium strain and NPSB fertilizer had the maximum grain production (3177.16 kg ha-1). It yielded 15.96%, 24.06%, and 37.93% more than the Arerti with CP-M41 strain, Arerti with NPSB, and the control treatments, respectively. The partial budget analysis of the study treatments showed that the Arerti variety with the combined application of 121 kg NPSB ha-1 and Mesorhizobium strain CP-M41 inoculation produced the highest net return (102,092.6 ETB ha-1) with an acceptable marginal rate of return (618%). It has been found that the CP-M41 strain and the Arerti variety, when combined with 121 kg NPSB ha-1 application, is a suitable treatment combination to achieve increased chickpea crop yield and profit in the studied area. However, the results need further validation in the farmer's field before recommending to farmers.

Early functional improvements using continuous passive motion therapy after angular-stable plate osteosynthesis of proximal humerus fractures - results of a prospective, randomized trial.

BACKGROUND: The use of continuous passive motion therapy (CPM) has led to promising results in the early phase of rehabilitation after surgical treatment of rotator cuff tears and arthrolysis of the elbow. However, its use has not been proven in other pathologies of the upper extremity. Therefore, the aim of the underlying study was to evaluate the use of CPM therapy after plate osteosynthesis of proximal humeral fractures. METHODS: 95 patients with isolated proximal humerus fractures were enrolled in a prospective, randomized study. Patients were assigned to a treatment group with (n = 48, CPM) or without CPM therapy (n = 47, CG). Four patients (2 of each cohort) violated the study protocol and were excluded. CPM therapy was used for 6 weeks after surgery 2-3 times daily. Functional (range of motion) and patient reported outcomes (PROM, Constant Score [CSS], QuickDASH, subjective shoulder value [SSV], pain on visual analogue scale [VAS]) were evaluated at 6 weeks, 3 and 12months. 60 patients completed the 1-year follow-up. RESULTS: The average patient age was 65.3 years (min: 27, max: 88, SD: ± 14.7). Seventy-two patients were female (79%). There was no difference regarding injury severity (2/3/4 part-fracture: 6/32/7 vs. 9/26/11, p = 0.867) and sex (p = 0.08). However, patients in the CPM group were significantly younger (CPM: 67 [min: 34, max: 82], CG: 74 [min: 27, max: 88], p = 0.032). After 6 weeks we observed a better range of motion for forward flexion (CPM: 90° [min: 50°, max: 180°] vs. CG: 80° [min: 20°, max: 170°] p = 0.035) and abduction (CPM: 80° [min: 40°, max: 180°] vs. CG: 70° [min: 20°, max: 180°], p = 0.048) in the CPM group. There was no difference regarding the further planes of motion or the assessed PROMs at 6 weeks. At 3 and 12 months the results between the treatment groups equalized with no further significant differences. CONCLUSION: The treatment with CPM increases the range of motion after plate osteosynthesis of proximal humerus fractures in the first 6 weeks after surgery. This effect is not sustained after 3 and 12months. The evaluated PROMs are not being influenced by CPM therapy. Hence the results of this prospective randomized study suggest that CPM can be a beneficial asset in the early period of rehabilitation after proximal humerus plate osteosynthesis. TRIAL REGISTRATION: The study protocol was registered in the US National Institutes of Health's database ( http://www. CLINICALTRIALS: gov ) registry under NCT05952622.

Evaluation of MRI post-processing methods combined with PET in detecting focal cortical dysplasia lesions for patients with MRI-negative epilepsy.

OBJECTIVE: Accurate detection of focal cortical dysplasia (FCD) through magnetic resonance imaging (MRI) plays a pivotal role in the preoperative assessment of epilepsy. The integration of multimodal imaging has demonstrated substantial value in both diagnosing FCD and devising effective surgical strategies. This study aimed to enhance MRI post-processing by incorporating positron emission tomography (PET) analysis. We sought to compare the diagnostic efficacy of diverse image post-processing methodologies in patients presenting MRI-negative FCD. METHODS: In this retrospective investigation, we assembled a cohort of patients with negative preoperative MRI results. T1-weighted volumetric sequences were subjected to morphometric analysis program (MAP) and composite parametric map (CPM) post-processing techniques. We independently co-registered images derived from various methods with PET scans. The alignment was subsequently evaluated, and its correlation was correlated with postoperative seizure outcomes. RESULTS: A total of 41 patients were enrolled in the study. In the PET-MAP(p = 0.0189) and PET-CPM(p = 0.00041) groups, compared with the non-overlap group, the overlap group significantly associated with better postoperative outcomes. In PET(p = 0.234), CPM(p = 0.686) and MAP(p = 0.672), there is no statistical significance between overlap and seizure-free outcomes. The sensitivity of using the CPM alone outperformed the MAP (0.65 vs 0.46). The use of PET-CPM demonstrated superior sensitivity (0.96), positive predictive value (0.83), and negative predictive value (0.91), whereas the MAP displayed superior specificity (0.71). CONCLUSIONS: Our findings suggested a superiority in sensitivity of CPM in detecting potential FCD lesions compared to MAP, especially when it is used in combination with PET for diagnosis of MRI-negative epilepsy patients. Moreover, we confirmed the superiority of synergizing metabolic imaging (PET) with quantitative maps derived from structural imaging (MAP or CPM) to enhance the identification of subtle epileptogenic zones (EZs). This study serves to illuminate the potential of integrated multimodal techniques in advancing our capability to pinpoint elusive pathological features in epilepsy cases.

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences.

High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity1-5. Further, they resemble task activation patterns and are well-studied3,5-10. However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing-a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP)11 and the UCLA Consortium for Neuropsychiatric Phenomics12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification14. They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brain's intrinsic functional architecture.

Potential Strategy to Control the Organic Components of Condensable Particulate Matter: A Critical Review.

More and more attention has been paid to condensable particulate matter (CPM) since its emissions have surpassed that of filterable particulate matter (FPM) with the large-scale application of ultralow-emission reform. CPM is a gaseous material in the flue stack but instantly turns into particles after leaving the stack. It is composed of inorganic and organic components. Organic components are an important part of CPM, and they are an irritant, teratogenic, and carcinogenic, which triggers photochemical smog, urban haze, and acid deposition. CPM organic components can aggravate air pollution and climate change; therefore, consideration should be given to them. Based on existing methods for removing atmospheric organic pollutants and combined with the characteristics of CPM organic components, we provide a critical overview from the aspects of (i) fundamental cognition of CPM, (ii) common methods to control CPM organic components, and (iii) catalytic oxidation of CPM organic components. As one of the most encouraging methods, catalytic oxidation is discussed in detail, especially in combination with selective catalytic reduction (SCR) technology, to meet the growing demands for multipollutant control (MPC). We believe that this review is inspiring for a fuller understanding and deeper exploration of promising approaches to control CPM organic components.