Tag: nevin manimala

Blood. 2022 Oct 7:blood.2022017814. doi: 10.1182/blood.2022017814. Online ahead of print.

ABSTRACT

Chronic lymphocytic Leukemia (CLL) and Monoclonal B-Lymphocytosis (MBL) patients have impaired response to COVID-19 vaccination. A total 258 patients (215 CLL and 43 MBL) had anti-spike levels evaluable for statistical analysis. The overall seroconversion rate for CLL was 94.2% (anti-spike ³50AU/mL Abbott Diagnostics) and for MBL 100%. After 3 doses (post-D3) in 167 CLL patients, 73.7% were seropositive, 17.4% had anti-spike levels 50-999AU/mL, and 56.3% ≥1000AU/mL with a median rise from 144.6AU/mL to 1800.7AU/mL. Of patients seronegative post-D2, 39.7% seroconverted post-D3. For those who then remained seronegative after their prior dose, seroconversion occurred in 40.6% post-D4, 46.2% post-D5, 16.7% post-D6, and 0% after D7 or D8. Following seroconversion, most had a progressive increment in anti-spike antibody level: in CLL after the latest dose, 70.2% achieved anti-spike level ≥1,000AU/mL, 48.1% ≥5,000AU/mL, and 30.3% ≥10,000AU/mL. Neutralization was associated with higher anti-spike levels, more vaccines and earlier COVID variants; 65.3% detected neutralizing antibody against early clade D614G, 52.0% against Delta, and 36.5% against Omicron. COVID-specific T-cell production of IFN-γ occurred in 73.9% and IL-2 in 60.9% of 23 tested, and more consistently with higher anti-spike levels. After multiple vaccine doses, by multivariate analysis, IgM ≥0.53g/L (OR=2.90, p=0.0314), IgG3 ≥0.22g/L (OR=3.26, p=0.0057), and lack of current CLL therapy (OR=2.48, p=0.0574) were independent predictors of positive serological responses. Strong neutralization and T-cell responses had high concordance with high anti-spike levels. Multiple sequential COVID-19 vaccination significantly increased seroconversion and anti-spike antibody levels in CLL and MBL.

PMID:36206503 | DOI:10.1182/blood.2022017814

Orthopedics. 2022 Oct 6:1-4. doi: 10.3928/01477447-20221003-03. Online ahead of print.

ABSTRACT

Distal femur fractures above a total knee arthroplasty (TKA) are challenging. These fractures can be fixed with a retrograde intramedullary nail (rIMN), but the design of the femoral component of the TKA influences the starting point for an rIMN. We performed a biomechanical study to evaluate how different TKA components influence the starting point for an rIMN and how that can lead to a deformity in the sagittal plane. We simulated a distal femur fracture with three different arthroplasty components. We used three different implants to simulate fracture reduction and measured the resultant sagittal plane deformity. Low and moderate femoral component ratio (FCR) design components were able to maintain fracture alignment within 5° of anatomic. High FCR component (more posterior starting point) sagittal plane deformities of up to 15° were observed with both the straight and medium Herzog bend nails, which was statistically significant (P<.001). Use of a high Herzog bend nail decreased the deformity by an average of 6°, which was statistically significant (P<.001). There is variability in how the TKA design affects the starting point and thus the sagittal plane alignment after fixation. This study helps quantify the effect of arthroplasty component design on fracture alignment. [Orthopedics. 20XX;XX(X):xx-xx.].

PMID:36206509 | DOI:10.3928/01477447-20221003-03

Exerc Sport Sci Rev. 2022 Oct 10. doi: 10.1249/JES.0000000000000308. Online ahead of print.

ABSTRACT

Researchers in neuromechanics should upgrade their statistical toolbox. We propose linear mixed-effects models in place of commonly used statistical tests to better capture subject-specific baselines and treatment-associated effects that naturally occur in neuromechanics. Researchers can use this approach to handle sporadic missing data, avoid the assumption of conditional independence in observations, and successfully model complex experimental protocols.

PMID:36206407 | DOI:10.1249/JES.0000000000000308

Dermatol Surg. 2022 Oct 6. doi: 10.1097/DSS.0000000000003601. Online ahead of print.

ABSTRACT

BACKGROUND: Extramammary Paget disease (EMPD) is a rare, slow growing neoplasm that presents most commonly in the anogenital region of older adults.

OBJECTIVE: To analyze the difference in local recurrence rates of EMPD in patients treated with wide local excision (WLE) versus Mohs micrographic surgery (MMS).

MATERIALS AND METHODS: A systematic review of the literature and meta-analysis were performed. Inclusion criteria were adults greater than 18 years of age with a diagnosis of EMPD who have undergone surgical intervention and had follow-up data. Studies were independently reviewed by 2 coinvestigators with discrepancies resolved by the principal investigator.

RESULTS: Twenty-seven studies met the inclusion criteria. Patients had a 2.67 times higher chance of local recurrence after WLE than MMS (95% confidence interval [CI]:1.47, 4.85; p = .001). Meta-analysis of single-arm studies revealed a 7.3% local recurrence rate after MMS (95% CI: 0.039, 0.107; p < .001) versus a 26.3% recurrence rate after WLE (95% CI: 0.149, 0.376; p < .001). After excluding recurrent tumors, the odds ratio for recurrence in WLE versus MMS was 2.3 (95% CI: 0.285, 18.43, p = .435).

CONCLUSION: There is a clinically and statistically increased risk of local recurrence of EMPD after WLE compared with MMS.

PMID:36206405 | DOI:10.1097/DSS.0000000000003601

J Pharm Pract. 2022 Oct 7:8971900221131933. doi: 10.1177/08971900221131933. Online ahead of print.

ABSTRACT

Background: Understanding risk factors for nonadherence can help specialty pharmacies optimize resources to prevent nonadherence and inform risk-stratification processes. Objective: To determine which individual and community-level characteristics are associated with nonadherence to specialty medications. Methods: We analyzed a cohort of patients enrolled in a prospective randomized controlled trial having filled a specialty medication at least 4 times in the previous 12 months with a proportion of days (PDC) covered < 0.90. We collected patient age, gender, race, medication administration type, therapy start date, home address, insurance type, and online patient portal status from the electronic health record. An ordinal logistic regression model was used to assess the association of nonadherence with individual and community-level patient characteristics. Results: Most patients were female (68%), white (82%), and held commercial insurance (58%) with a median age of 53 (interquartile range [IQR] 40, 64) years. Patients were mostly from the adult rheumatology (35%), multiple sclerosis (20%) and lipid (17%) clinics. Given a 10-year increase in age, patients had lower odds of having lower PDC (odds ratio [OR] = 0.82, 95% confidence interval [CI] = 0.71-0.94, P = 0.005). Patients on therapy greater than or equal to 1 year had half the odds of having lower PDC relative to patients on therapy less than 1 year (OR = 0.52, CI = 0.35 – 0.75, P < 0.001). No statistically significant associations were found between PDC and gender, race, insurance type, route of administration, clinic type, patient portal status, median income, percent receiving government assistance, or percent with no health insurance. Conclusion: Patients with younger age and shorter duration on treatment may be at-risk for lower adherence. Specialty pharmacies may benefit from targeting adherence interventions to these groups.

PMID:36206399 | DOI:10.1177/08971900221131933

Hum Brain Mapp. 2022 Oct 7. doi: 10.1002/hbm.26105. Online ahead of print.

ABSTRACT

Understanding the impact of variation in lesion topography on the expression of functional impairments following stroke is important, as it may pave the way to modeling structure-function relations in statistical terms while pointing to constraints for adaptive remapping and functional recovery. Multi-perturbation Shapley-value analysis (MSA) is a relatively novel game-theoretical approach for multivariate lesion-symptom mapping. In this methodological paper, we provide a comprehensive explanation of MSA. We use synthetic data to assess the method’s accuracy and perform parameter optimization. We then demonstrate its application using a cohort of 107 first-event subacute stroke patients, assessed for upper limb (UL) motor impairment (Fugl-Meyer Assessment scale). Under the conditions tested, MSA could correctly detect simulated ground-truth lesion-symptom relationships with a sensitivity of 75% and specificity of ~90%. For real behavioral data, MSA disclosed a strong hemispheric effect in the relative contribution of specific regions-of-interest (ROIs): poststroke UL motor function was mostly contributed by damage to ROIs associated with movement planning (supplementary motor cortex and superior frontal gyrus) following left-hemispheric damage (LHD) and by ROIs associated with movement execution (primary motor and somatosensory cortices and the ventral brainstem) following right-hemispheric damage (RHD). Residual UL motor ability following LHD was found to depend on a wider array of brain structures compared to the residual motor ability of RHD patients. The results demonstrate that MSA can provide a unique insight into the relative importance of different hubs in neural networks, which is difficult to obtain using standard univariate methods.

PMID:36206326 | DOI:10.1002/hbm.26105

Hum Brain Mapp. 2022 Oct 7. doi: 10.1002/hbm.26098. Online ahead of print.

ABSTRACT

Although a large number of case-control statistical and machine learning studies have been conducted to investigate structural brain changes in schizophrenia, how best to measure and characterize structural abnormalities for use in classification algorithms remains an open question. In the current study, a convolutional 3D autoencoder specifically designed for discretized volumes was constructed and trained with segmented brains from 477 healthy individuals. A cohort containing 158 first-episode schizophrenia patients and 166 matched controls was fed into the trained autoencoder to generate auto-encoded morphological patterns. A classifier discriminating schizophrenia patients from healthy controls was built using 80% of the samples in this cohort by automated machine learning and validated on the remaining 20% of the samples, and this classifier was further validated on another independent cohort containing 77 first-episode schizophrenia patients and 58 matched controls acquired at a different resolution. This specially designed autoencoder allowed a satisfactory recovery of the input. With the same feature dimension, the classifier trained with autoencoded features outperformed the classifier trained with conventional morphological features by about 10% points, achieving 73.44% accuracy and 0.8 AUC on the internal validation set and 71.85% accuracy and 0.77 AUC on the external validation set. The use of features automatically learned from the segmented brain can better identify schizophrenia patients from healthy controls, but there is still a need for further improvements to establish a clinical diagnostic marker. However, with a limited sample size, the method proposed in the current study shed insight into the application of deep learning in psychiatric disorders.

PMID:36206321 | DOI:10.1002/hbm.26098

J Nerv Ment Dis. 2022 Oct 10. doi: 10.1097/NMD.0000000000001464. Online ahead of print.

ABSTRACT

In December 2019, some pneumonia cases emerged in Wuhan, China. It was named as Coronavirus 2019 (COVID-19) by the World Health Organization.Patients developed anxiety and sleep problems after treatment. Patients with confirmed COVID-19 (n = 57) admitted to our study and whose treatment was completed 3 months ago were included in the study. This is a case-control study, and 22 patients included the control group. We found statistical significance between the average score of Beck anxiety and Pittsburgh Sleep Quality Index (p = 0.03, p = 0.01).In our study, we investigated the psychological conditions that may occur in the postacute COVID-19 period. We recommend that patients should be directed to appropriate clinics for rehabilitation. Clinicians must be aware that prompt and correct diagnosis with careful management is essential for recovery.

PMID:36206312 | DOI:10.1097/NMD.0000000000001464

PLoS One. 2022 Oct 7;17(10):e0261484. doi: 10.1371/journal.pone.0261484. eCollection 2022.

ABSTRACT

High field MRI is an advanced technique for diagnostic and research purposes on animal models, such as the Beagle dog. In this context, studies on neuroscience applications, e.g. aging and neuro-pathologies, are currently increasing. This led to a need for reference values, in terms of volumetric assessment, for the structures typically involved. Nowadays, several canine brain MRI atlases have been provided. However, no reports are available regarding the measurements’ reproducibility and little is known about the effect of formalin on MRI segmentation. Here, we assessed the segmentation variability of selected structures among operators (two operators segmented the same data) in a sample of 11 Beagle dogs. Then, we analyzed, for one Beagle dog, the longitudinal volumetric changes of these structures. We considered four conditions: in vivo, post mortem (after euthanasia), ex vivo (brain extracted and studied after 1 month in formalin, and after 12 months). The MRI data were collected with a 3 T scanner. Our findings suggest that the segmentation procedure was overall reproducible since only slight statistical differences were detected. In the post mortem/ ex vivo comparison, most structures showed a higher contrast, thereby leading to greater reproducibility between operators. We observed a net increase in the volume of the studied structures. This could be justified by the intrinsic relaxation time changes observed because of the formalin fixation. This led to an improvement in brain structure visualization and segmentation. To conclude, MRI-based segmentation seems to be a useful and accurate tool that allows longitudinal studies on formalin-fixed brains.

PMID:36206292 | DOI:10.1371/journal.pone.0261484

PLoS One. 2022 Oct 7;17(10):e0275619. doi: 10.1371/journal.pone.0275619. eCollection 2022.

ABSTRACT

BACKGROUND: Multimorbidity is a worldwide concern related to greater disability, worse quality of life, and mortality. The early prediction is crucial for preventive strategies design and integrative medical practice. However, knowledge about how to predict multimorbidity is limited, possibly due to the complexity involved in predicting multiple chronic diseases.

METHODS: In this study, we present the use of a machine learning approach to build cost-effective multimorbidity prediction models. Based on predictors easily obtainable in clinical practice (sociodemographic, clinical, family disease history and lifestyle), we build and compared the performance of seven multilabel classifiers (multivariate random forest, and classifier chain, binary relevance and binary dependence, with random forest and support vector machine as base classifiers), using a sample of 15105 participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). We developed a web application for the building and use of prediction models.

RESULTS: Classifier chain with random forest as base classifier performed better (accuracy = 0.34, subset accuracy = 0.15, and Hamming Loss = 0.16). For different feature sets, random forest based classifiers outperformed those based on support vector machine. BMI, blood pressure, sex, and age were the features most relevant to multimorbidity prediction.

CONCLUSIONS: Our results support the choice of random forest based classifiers for multimorbidity prediction.

PMID:36206287 | DOI:10.1371/journal.pone.0275619