Category: Nevin Manimala

Urolithiasis. 2024 Feb 20;52(1):36. doi: 10.1007/s00240-024-01531-0.

ABSTRACT

Kidney stones have a prevalence rate of > 10% in some countries. There has been a significant increase in surgery to treat kidney stones over the last 10 years, and it is crucial that such techniques are as effective as possible, while limiting complications. A selection of kidney stones with different chemical and structural properties were subjected to compression. Under compression, they emit acoustic signals called crackling noise. The variability of the crackling noise was surprisingly great comparing weddellite, cystine and uric acid stones. Two types of signals were found in all stones. At high energies of the emitted sound waves, we found avalanche behaviour, while all stones also showed signals of local, uncorrelated collapse. These two types of events are called ‘wild’ for avalanches and ‘mild’ for uncorrelated events. The key observation is that the crossover from mild to wild collapse events differs greatly between different stones. Weddellite showed brittle collapse, extremely low crossover energies (< 5 aJ) and wild avalanches over 6 orders of magnitude. In cystine and uric acid stones, the collapse was more complicated with a dominance of local “mild” breakings, although they all contained some stress-induced collective avalanches. Cystine stones had high crossover energies, typically [Formula: see text] 750 aJ, and a narrow window over which they showed wild avalanches. Uric acid stones gave moderate values of crossover energies, [Formula: see text] 200 aJ, and wild avalanche behaviour for [Formula: see text] 3 orders of magnitude. Further research extended to all stone types, and measurement of stone responses to different lithotripsy strategies, will assist in optimisation of settings of the laser and other lithotripsy devices to insight fragmentation by targeting the ‘wild’ avalanche regime.

PMID:38376662 | DOI:10.1007/s00240-024-01531-0

Int Urol Nephrol. 2024 Feb 20. doi: 10.1007/s11255-024-03940-x. Online ahead of print.

ABSTRACT

PURPOSE: Considering the importance of incorporating quality of life (QoL) construct during the health care of patients with stage 5 chronic kidney disease (CKD) on dialysis, it is necessary to have evidence on the clinimetric properties of the instruments used for its measurement. This study aimed to establish the clinimetric properties of the Kidney Disease Quality of Life Short Form 36 (KDQOL-36) scale in patients with stage 5 CKD on dialysis in Colombia.

METHODS: A scale validation study was conducted using the classical test theory methodology. The statistical analysis included exploratory factor analysis (EFA) and confirmatory (CFA) techniques performed on two independent subsamples; concurrent criterion validity assessments; internal consistency using four different coefficients; test-retest reliability; and sensitivity to change using mixed model for repeated measures.

RESULTS: The KDQOL-36 scale was applied to 506 patients with a diagnosis of stage 5 CKD on dialysis, attended in five renal units in Colombia. The EFA endorsed the three-factor structure of the scale, and the CFA showed an adequate fit of both the original and empirical models. Spearman’s correlation coefficient values ≥0.50 were found between the domains of the CKD-specific core of the KDQOL-36 scale and the KDQ. Cronbach’s alpha, McDonald’s omega, Greatest lower bound (GLB), and Guttman’s lambda coefficients were ≥0.89, indicating a high degree of consistency. A high level of concordance correlation was found between the two moments of application of the instrument, with values for Lin’s concordance correlation coefficient ≥0.7. The application of the instrument after experiencing an event that could modify the quality of life showed statistically significant differences in the scores obtained.

CONCLUSION: The KDQOL-36 scale is an adequate instrument for measuring QoL in Colombian patients with stage 5 CKD on dialysis.

PMID:38376660 | DOI:10.1007/s11255-024-03940-x

Int Urol Nephrol. 2024 Feb 20. doi: 10.1007/s11255-024-03959-0. Online ahead of print.

ABSTRACT

OBJECTIVE: In this study, we aimed to evaluate the combined diagnostic value of procalcitonin (PCT), C-reactive protein (CRP), and serum amyloid A (SAA) in sepsis caused by urinary tract infection.

METHOD: A total of 80 patients with urosepsis who were hospitalized were included in the study group, and 80 patients with urinary tract infection without sepsis were included in the control group. We collected the PCT, SAA, and CRP levels of patients following admission. Subsequently, we conducted a comparative analysis to assess the specificity, accuracy, and sensitivity of combined diagnostic approaches in contrast to individual diagnostic methods for blood PCT, SAA, and CRP.

RESULTS: The levels of PCT, SAA, and CRP in the study group were significantly higher than those in the control group, and the differences were statistically significant (P < 0.01). Multi-factor logistic regression analysis revealed that the levels of PCT (P = 0.003) and SAA (P = 0.014) were associated with urosepsis. The sensitivity of PCT was 87.133% and the specificity was 93.066%, which were higher than that of SAA and CRP. The specificity of the combined detection of the three was 95.670%, which was higher than that of PCT, SAA, and CRP alone. Correlation analysis revealed that PCT had a significant positive correlation with CRP and SAA (P < 0.01), and a weak correlation with white blood cell count (WBC) and fibrinogen (FIB) (P = 0.03 for WBC, P = 0.04 for FIB).

CONCLUSION: PCT, SAA, and CRP indicators in patients with urosepsis are significantly elevated, and all three are valuable in the diagnosis of urosepsis. PCT alone has good diagnostic efficiency for urosepsis, and a certain correlation with other inflammatory factors. The diagnostic efficacy of the three indicators in combination is better than that of any one of the three, and is worthy of widespread clinical application.

PMID:38376659 | DOI:10.1007/s11255-024-03959-0

J Psychiatr Res. 2024 Feb 14;172:108-118. doi: 10.1016/j.jpsychires.2024.02.032. Online ahead of print.

ABSTRACT

In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.

PMID:38373372 | DOI:10.1016/j.jpsychires.2024.02.032

Cytokine. 2024 Feb 18;177:156547. doi: 10.1016/j.cyto.2024.156547. Online ahead of print.

ABSTRACT

BACKGROUND: Epidemiological and experimental evidences have implicated chronic inflammation in the association with allergic rhinitis (AR). However, it remains unclear whether specific circulating cytokines are the cause of AR or the consequence of bias. To examine whether genetic-predicted changes in circulating cytokine concentrations are related to the occurrence of AR, we conducted a two-sample Mendelian randomization (MR) analysis.

METHODS: We investigated the causal effects of 26 circulating inflammatory cytokines on AR through MR analysis. The primary method employed in this study was the inverse variance-weighted (IVW) method. Sensitivity analyses were conducted using simple median, weighted median, penalized weighted median, and MR-Egger regression.

RESULTS: Our study revealed suggestive evidence that higher levels of circulating IL-18 (OR per one standard deviation [SD] increase: 1.006; 95 % CI, 1.002 to 1.011; P = 0.006, P_FDR = 0.067, random-effects IVW method) and Macrophage inflammatory protein-1α (MIP-1α) (OR per one SD increase: 1.015; 95 % CI, 1.004 to 1.026; P = 0.009, P_FDR = 0.048, random-effects IVW method) were associated with an increased risk of AR. Conversely, higher levels of circulating TRAIL were associated with a decreased risk of AR (OR per one SD increase: 0.993; 95 % CI, 0.989 to 0.997; P = 4.58E-4, P_FDR = 0.004, random-effects IVW method). Only the results of TRAIL exist after Bonferroni-correction (the p-value < 0.0019). Sensitivity analysis yielded directionally consistent results. No significant associations were observed between other circulating inflammatory cytokines and AR.

CONCLUSION: Genetically predicted levels of IL-18, and MIP-1α are likely to associated with an increased risk of AR occurrence. Genetically predicted levels of TRAIL are statistically significant in reducing the risk of AR occurrence. However, the current research evidence does not support an impact of other inflammatory cytokines on the risk of AR. Future studies are needed to provide additional evidence to support the current conclusions.

PMID:38373366 | DOI:10.1016/j.cyto.2024.156547

Ann Intern Med. 2024 Feb 20. doi: 10.7326/M23-2430. Online ahead of print.

NO ABSTRACT

PMID:38373303 | DOI:10.7326/M23-2430

Environ Sci Technol. 2024 Feb 19. doi: 10.1021/acs.est.3c10490. Online ahead of print.

ABSTRACT

Alzheimer’s disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ_1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.

PMID:38373301 | DOI:10.1021/acs.est.3c10490

J Appl Clin Med Phys. 2024 Feb 19:e14297. doi: 10.1002/acm2.14297. Online ahead of print.

ABSTRACT

PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical image segmentation applications. However, CNNs have limitations in learning long-range spatial dependencies due to the locality of the convolutional layers. Transformers were introduced to address this challenge. In transformers with self-attention mechanism, even the first layer of information processing makes connections between distant image locations. Our paper presents a novel framework that bridges these two unique techniques, CNNs and transformers, to segment the gross tumor volume (GTV) accurately and efficiently in computed tomography (CT) images of non-small cell-lung cancer (NSCLC) patients.

METHODS: Under this framework, input of multiple resolution images was used with multi-depth backbones to retain the benefits of high-resolution and low-resolution images in the deep learning architecture. Furthermore, a deformable transformer was utilized to learn the long-range dependency on the extracted features. To reduce computational complexity and to efficiently process multi-scale, multi-depth, high-resolution 3D images, this transformer pays attention to small key positions, which were identified by a self-attention mechanism. We evaluated the performance of the proposed framework on a NSCLC dataset which contains 563 training images and 113 test images. Our novel deep learning algorithm was benchmarked against five other similar deep learning models.

RESULTS: The experimental results indicate that our proposed framework outperforms other CNN-based, transformer-based, and hybrid methods in terms of Dice score (0.92) and Hausdorff Distance (1.33). Therefore, our proposed model could potentially improve the efficiency of auto-segmentation of early-stage NSCLC during the clinical workflow. This type of framework may potentially facilitate online adaptive radiotherapy, where an efficient auto-segmentation workflow is required.

CONCLUSIONS: Our deep learning framework, based on CNN and transformer, performs auto-segmentation efficiently and could potentially assist clinical radiotherapy workflow.

PMID:38373289 | DOI:10.1002/acm2.14297

J Phys Chem A. 2024 Feb 19. doi: 10.1021/acs.jpca.3c06894. Online ahead of print.

ABSTRACT

Computational quantum chemical techniques were utilized to systematically examine how electron-donating groups affect the electronic and spectroscopic properties of halogen bond donors and their corresponding complexes. Unlike the majority of studies on halogen bonding, where electron-withdrawing groups are utilized, this work investigates the influence of electron-donating substituents within the halogen bond donors. Statistical analyses were performed on the descriptors of halogen bond donors in a prescribed set of archetype, halo-alkyne, halo-benzene, and halo-ethynyl benzene halogen bond systems. The σ-hole magnitude, binding and interaction energies, and the vibrational X···N local force constant (where X = Cl, Br, I, and At) were found to correlate very well in a monotonic and linear manner with all other properties studied. In addition, enhanced halogen bonds were found when the systems contained electron-donating groups that could form intramolecular hydrogen bonds with the electronegative belt of the halogen atom and adjacent linker features.

PMID:38373286 | DOI:10.1021/acs.jpca.3c06894

Ann Work Expo Health. 2024 Feb 19:wxae008. doi: 10.1093/annweh/wxae008. Online ahead of print.

ABSTRACT

INTRODUCTION: Upper respiratory tract infections (URTI) are common and a common cause of sick-leave for healthcare workers, and furthermore pose a threat especially for patients susceptible to other diseases. Sufficient use of respiratory protective equipment (RPE) may protect both the workers and the patients. The COVID-19 pandemic provided a unique opportunity to study the association between use of RPE and URTI in a real-life setting. The aim of this study was to examine if failure of RPE or non-compliance with RPE guidelines increases the risk of non-COVID-19 URTI symptoms among healthcare workers.

METHODS: In a longitudinal cohort study, we collected self-reported data daily on work tasks, use of RPE, and URTI symptoms among healthcare workers with patient contact in 2 Danish Regions in 2 time periods during the COVID-19 pandemic. The association between failure of RPE or non-compliance with RPE guidelines and URTI symptoms was analyzed separately by generalized linear models. Persons tested positive for severe acute respiratory syndrome coronavirus 2 were censored from the analyses. The 2 waves of data collection were analyzed separately, as there were differences in recommendations of RPE during the 2 waves.

RESULTS: We found that for healthcare workers performing work tasks with a risk of transmission of viruses or bacteria, failure of RPE was associated with an increased risk of URTI symptoms, RR: 1.65[0.53-5.14] in wave 1 and RR: 1.30[0.56-3.03] in wave 2. Also non-compliance with RPE guidelines was associated with an increased risk of URTI symptoms compared to the use of RPE in wave 1, RR: 1.28[0.87-1.87] and wave 2, RR: 1.39[1.01-1.91]. Stratifying on high- versus low-risk tasks showed that the risk related to failure and non-compliance was primarily associated with high-risk tasks, although not statistically significant.

DISCUSSION: The study was conducted during the COVID-19 pandemic and thus may be affected by other preventive measures in society. However, this gave the opportunity to study the use of RPE in a real-life setting, also in departments that did not previously use RPE. The circumstances in the 2 time periods of data collection differed and were analyzed separately and thus the sample size was limited and affected the precision of the estimates.

CONCLUSION: Failures of RPE and non-compliance with RPE guidelines may increase the risk of URTI, compared to those who reported use of RPE as recommended. The implications of these findings are that the use of RPE to prevent URTI could be considered, especially while performing high-risk tasks where other prevention strategies are not achievable.

PMID:38373246 | DOI:10.1093/annweh/wxae008