Tag: pubmed

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

Free Radic Res. 2024 Feb 19:1-12. doi: 10.1080/10715762.2024.2320381. Online ahead of print.

ABSTRACT

INTRODUCTION: Prostate Cancer (PC) is a global health concern affecting men worldwide. Oxidative stress is believed to contribute to the initiation of early-stage PC lesions. Additionally, inflammation has long been acknowledged as a factor in the development of PC. We aimed to examine the biomarkers of oxidative stress and inflammation in PC patients before and after surgery.

PATIENTS AND METHODS: A cross-sectional study was conducted at the Urology Outpatient Clinic of Bezmialem Vakif University Hospital. A total of 150 individuals were included in the study, divided into five groups: 50 Healthy controls, 25 patients with Benign Prostatic Hyperplasia (BPH), 25 patients with Low-Risk Prostate Cancer (LRPC), 25 patients with Medium-Risk Prostate Cancer (MRPC), and 25 patients with High-Risk Prostate Cancer (HRPC). Measurements of Total Oxidant Status (TOS), Total Antioxidant Status (TAS), Total Thiol (TT), and Native Thiol (NT) were performed using photometric methods. Oxidative Stress Index (OSI) and Disulfide (DIS) levels were calculated mathematically. Levels of Interleukin-10 (IL-10), Interleukin-1beta (IL-1β), Tumor Necrosis Factor-alpha (TNF-α), Interleukin-6 (IL-6), and Presepsin were determined using commercially available enzyme-linked immunosorbent assay (ELISA) kits.

RESULTS: Compared to the healthy control group, the results indicated a statistically significant increase in both oxidative stress and inflammation levels. In the groups receiving both pharmaceutical therapy and surgical treatment (PC), a significant decrease in oxidative stress and inflammation levels was observed.

CONCLUSION: Consequently, it is suggested that the assessment of oxidative stress and inflammatory biomarkers should be incorporated in the pre- and postoperative monitoring of patients with PC.

PMID:38373238 | DOI:10.1080/10715762.2024.2320381

Urogynecology (Phila). 2024 Jan 16. doi: 10.1097/SPV.0000000000001447. Online ahead of print.

ABSTRACT

IMPORTANCE: Rectovaginal fistula (RVF) is a challenging condition associated with recurrences and significant functional impairment.

OBJECTIVES: The internal pudendal artery perforator (IPAP) flap has become a viable option for reconstructing the vagina and perineal regions. This study aims to introduce a modified technique of IPAP flap interposition and evaluate its postoperative outcomes in the treatment of low RVF.

STUDY DESIGN: Sixteen patients with RVF who underwent modified IPAP flap interposition between 2016 and 2021 were retrospectively enrolled. Recurrence rate, the satisfaction of vulvar appearance (Visual Analog Scale), and quality of sexual life (Female Sexual Function Index score) were followed up and analyzed.

RESULTS: All patients presented with low fistula with a mean diameter of 8.3 mm. The mean width and length of the IPAP flaps were 3.8 and 6.2 cm, respectively. The mean follow-up period was 14.1 months. All patients achieved successful healing without recurrence. High satisfaction was reported for the cosmetic effect of the vulva with a mean Visual Analog Scale score of 8.4. The proportion of female sexual disorder exhibited a statistically significant reduction, decreasing from 100% preoperatively to 38% after surgery (P < 0.05).

CONCLUSIONS: The modified IPAP flap interposition is a reliable and safe option for repairing low RVF, with high success rates and minimal donor site morbidity. Moreover, this procedure provides a suitable volume flap and preserves the vaginal physiological environment, which benefits postoperative sexual function.

PMID:38373234 | DOI:10.1097/SPV.0000000000001447