Tag: pubmed

In Vivo. 2024 May-Jun;38(3):1429-1435. doi: 10.21873/invivo.13585.

ABSTRACT

BACKGROUND/AIM: Stage migration, a phenomenon triggered by technological advancements allowing more sensitive tumor spread detection, results in alterations in the distribution of cancer stages within a population. Canine multicentric lymphoma is staged I to V based on the affected anatomic site(s) and substage a or b depending on the presence of tumor-related clinical signs. The primary objective of this study was to assess the influence of various diagnostic techniques on staging accuracy and determine whether multiple staging methods lead to significant stage migration, impacting the reliability of disease stage assignments.

MATERIALS AND METHODS: Dogs cytologically diagnosed with multicentric lymphoma were staged using four different staging methods (A-D): A (physical examination, hemogram, blood smear), B (A plus thoracic X-ray, abdominal ultrasound), C (B plus liver and spleen cytology) and D (C plus bone marrow cytology).

RESULTS: Twenty-three dogs were enrolled: 16 females (70%) and seven males (30%). Regarding immunophenotype, 21 dogs (91.3%) were B-cell and two dogs (8.7%) were T-cell. Stage migration was observed between all staging methods. Between A and B, 12 animals migrated from stage III to stage IV. Between B and C, four animals migrated, three to a higher stage (stage III to IV) and one to a lower stage (stage IV to III). Between C and D, one animal migrated from stage IV to V. The differences between staging methods A and B were statistically significant (p≤0.001).

CONCLUSION: Stage migration in canine multicentric lymphoma depends on the diagnostic methods used and reinforces the need to use standardized staging methods to avoid it.

PMID:38688606 | DOI:10.21873/invivo.13585

In Vivo. 2024 May-Jun;38(3):1260-1265. doi: 10.21873/invivo.13563.

ABSTRACT

BACKGROUND/AIM: Endometrial cancer (EC) is the predominant malignancy among gynecologic cancers and ranks fourth among all types of cancer. Recently, researchers have focused on the development of new prognostic biomarkers. Subunits of the SWI/SNF protein complex, like the ARID1 and BRG1, have been associated with the development of endometrial cancer. The present study aimed to evaluate the expression patterns of ARID1A and BRG1 in a collection of endometrioid adenocarcinomas of the uterus using immunohistochemistry.

PATIENTS AND METHODS: The study comprised a total of thirty-three individuals diagnosed with stage I endometrioid endometrial cancer, treated with radical hysterectomy. The histological material was then examined to assess the cytoplasmic and nuclear expression of the proteins.

RESULTS: ARID1A exhibited expression in both the cytoplasm and nucleus of cancer cells, whereas BRG1 was mainly expressed in the nuclei. In addition, ARID1A exhibited a notable decrease in expression in grade 3 histology, with no significant correlation with the depth of myometrial invasion. The reduced expression was highly related to tumor expansion into the endocervix. The findings demonstrated a total absence of ARID1A expression in 27% of endometrioid carcinomas, with a significant reduction in expression in an additional 51% of cancer cells. These findings align with the most recent published data. In contrast, in the current study, BRG1 was rarely down-regulated and was extensively expressed in the majority of endometrioid carcinomas, preventing the possibility of statistical analysis.

CONCLUSION: In summary, ARID1A expression loss can be used as a biomarker to guide post-operative therapy; however, further investigation is needed, especially for early-stage endometrial cancer.

PMID:38688602 | DOI:10.21873/invivo.13563

In Vivo. 2024 May-Jun;38(3):1359-1366. doi: 10.21873/invivo.13576.

ABSTRACT

BACKGROUND/AIM: Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting.

PATIENTS AND METHODS: Clinical variables of patients treated in three Institutions with SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients). A predictive model was built using Cox regression (CR) and artificial neural networks (ANN) on reference cohort A and then tested on comparative cohorts.

RESULTS: Cohort B1 patients were older and with worse chronic obstructive pulmonary disease (COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charlson Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The model was enhanced combining ANN and CR findings. The reference cohort was divided into prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model’s predictions on OS: grouping was close to statistical significance (p=0.081). One and 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohorts, the model successfully predicted two groups of patients with divergent OS trends: higher for Group 1 and lower for Group 2.

CONCLUSION: The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when applied to different cohorts. ANN are a valuable resource, providing useful data to build a prognostic model that deserves to be validated prospectively.

PMID:38688600 | DOI:10.21873/invivo.13576

J Proteome Res. 2024 Apr 30. doi: 10.1021/acs.jproteome.3c00902. Online ahead of print.

ABSTRACT

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, “open modification” database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional “narrow window” database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.‘s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

PMID:38687997 | DOI:10.1021/acs.jproteome.3c00902

JMIR Public Health Surveill. 2024 Apr 30;10:e54769. doi: 10.2196/54769.

ABSTRACT

BACKGROUND: The unprecedented emergence of the COVID-19 pandemic necessitated the development and global distribution of vaccines, making the understanding of global vaccine acceptance and hesitancy crucial to overcoming barriers to vaccination and achieving widespread immunization.

OBJECTIVE: This umbrella review synthesizes findings from systematic reviews and meta-analyses to provide insights into global perceptions on COVID-19 vaccine acceptance and hesitancy across diverse populations and regions.

METHODS: We conducted a literature search across major databases to identify systematic reviews and meta-analysis that reported COVID-19 vaccine acceptance and hesitancy. The AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews) criteria were used to assess the methodological quality of included systematic reviews. Meta-analysis was performed using STATA 17 with a random effect model. The data synthesis is presented in a table format and via a narrative.

RESULTS: Our inclusion criteria were met by 78 meta-analyses published between 2021 and 2023. Our analysis revealed a moderate vaccine acceptance rate of 63% (95% CI 0.60%-0.67%) in the general population, with significant heterogeneity (I² = 97.59%). Higher acceptance rates were observed among health care workers and individuals with chronic diseases, at 64% (95% CI 0.57%-0.71%) and 69% (95% CI 0.61%-0.76%), respectively. However, lower acceptance was noted among pregnant women, at 48% (95% CI 0.42%-0.53%), and parents consenting for their children, at 61.29% (95% CI 0.56%-0.67%). The pooled vaccine hesitancy rate was 32% (95% CI 0.25%-0.39%) in the general population. The quality assessment revealed 19 high-quality, 38 moderate-quality, 15 low-quality, and 6 critically low-quality meta-analyses.

CONCLUSIONS: This review revealed the presence of vaccine hesitancy globally, emphasizing the necessity for population-specific, culturally sensitive interventions and clear, credible information dissemination to foster vaccine acceptance. The observed disparities accentuate the need for continuous research to understand evolving vaccine perceptions and to address the unique concerns and needs of diverse populations, thereby aiding in the formulation of effective and inclusive vaccination strategies.

TRIAL REGISTRATION: PROSPERO CRD42023468363; https://tinyurl.com/2p9kv9cr.

PMID:38687992 | DOI:10.2196/54769

JMIR Form Res. 2024 Apr 30;8:e54120. doi: 10.2196/54120.

ABSTRACT

BACKGROUND: Digital health technologies offer the potential to improve the daily lives of older adults, maintain their health efficiently, and allow aging in place. Despite increasing evidence of benefits and advantages, readiness for adopting digital interventions among older people remains underexplored.

OBJECTIVE: This study aims to explore the relationships between sociodemographic-, health-, and lifestyle-related factors and technology use in everyday life and community-dwelling older adults’ readiness to adopt telemedicine, smartphones with texting apps, wearables, and robotics.

METHODS: This was a cross-sectional, population-based survey study with a stratified probabilistic sample of adults aged 75 years or older living in South Tyrol (autonomous province of Bolzano/Bozen, Italy). A random sample of 3600 community-dwelling older adults living at home was invited to complete a questionnaire including single items (older adults’ readiness to use health technology) and scales (PRISMA-7; Program of Research on Integration of Services for the Maintenance of Autonomy). Descriptive and logistic regression analyses were performed to analyze the data.

RESULTS: In total, 1695 community-dwelling older adults completed the survey (for a response rate of 47%). In terms of potential digital health technology adoption, wearable devices were favored by 33.7% (n=571), telemedicine by 30.1% (n=510), smartphones and texting apps by 24.5% (n=416), and assistant robots by 13.7% (n=232). Sociodemographic-, health- and lifestyle-related factors, as well as the use of technology in everyday life, played a significant role in explaining readiness to adopt digital health technologies. For telemedicine, age ≥85 years (odds ratio [OR] 0.74, 95% CI 0.56-0.96), financial constraints (OR 0.68, 95% CI 0.49-0.95), and less than 2 hours of physical activity per week (OR 0.75, 95% CI 0.58-0.98) were associated with nonreadiness, while Italian-speaking participants (OR 1.54, 95% CI 1.16-2.05) and those regularly using computers (OR 1.74, 95% CI 1.16-2.60), smartphones (OR 1.69, 95% CI 1.22-2.35), and the internet (OR 2.26, 95% CI 1.47-3.49) reported readiness for adoption.

CONCLUSIONS: Community-dwelling older adults display varied readiness toward the adoption of digital health technologies, influenced by age, mother tongue, living situation, financial resources, physical activity, and current use of technology. The findings underscore the need for tailored interventions and educational programs to boost digital health technology adoption among community-dwelling older adults.

PMID:38687989 | DOI:10.2196/54120

Orbit. 2024 Apr 30:1-10. doi: 10.1080/01676830.2024.2343299. Online ahead of print.

ABSTRACT

PURPOSE: The posterior orbit is a confined space, harbouring neurovascular structures, frequently distorted by tumours. Image-guided navigation (IGN) has the potential to allow accurate localisation of these lesions and structures, reducing collateral damage whilst achieving surgical objectives.

METHODS: We assessed the feasibility, effectiveness and safety of using an electromagnetic IGN for posterior orbital tumour surgery via a comparative cohort study. Outcomes from cases performed with IGN were compared with a retrospective cohort of similar cases performed without IGN, presenting a descriptive and statistical comparative analysis.

RESULTS: Both groups were similar in mean age, gender and tumour characteristics. IGN set-up and registration were consistently achieved without significant workflow disruption. In the IGN group, fewer lateral orbitotomies (6.7% IGN, 46% non-IGN), and more transcutaneous lid and transconjunctival incisions (93% IGN, 53% non-IGN) were performed (p = .009). The surgical objective was achieved in 100% of IGN cases, with no need for revision surgery (vs 23% revision surgery in non-IGN, p = .005). There was no statistically significant difference in surgical complications.

CONCLUSION: The use of IGN was feasible and integrated into the orbital surgery workflow to achieve surgical objectives more consistently and allowed the use of minimal access approaches. Future multicentre comparative studies are needed to explore the potential of this technology further.

PMID:38687963 | DOI:10.1080/01676830.2024.2343299

J Cancer Res Ther. 2024 Apr 1;20(2):706-711. doi: 10.4103/jcrt.jcrt_498_23. Epub 2024 Apr 30.

ABSTRACT

BACKGROUND: Oral submucous fibrosis (OSF) is a precancerous lesion, with oral squamous cell carcinoma (OSCC) being the most prevalent malignancy affecting the oral mucosa. The malignant transformation of OSF into OSCC is estimated to occur in 7-13% of cases. Myofibroblasts (MFs) play pivotal roles in both physiological and pathological processes, such as wound healing and tumorigenesis, respectively. This study aimed to explore the involvement of MFs in the progression of OSF and its malignant transformation.

MATERIALS AND METHODS: In total, 94 formalin-fixed paraffin-embedded tissue blocks were collected, including normal oral mucosa (NOM; n = 10), early-moderate OSF (EMOSF; n = 29), advanced OSF (AOSF; n = 29), paracancerous OSF (POSF; n = 21), and OSCC (n = 5) samples. Alpha-smooth muscle actin was used for the immunohistochemical identification of MFs.

RESULTS: NOM exhibited infrequent expression of MFs. A higher staining index of MFs was found in AOSF, followed by EMOSF and NOM. Additionally, a significant increase in the staining index of MFs was found from EMOSF to POSF and OSCC. The staining index of MFs in NOM, EMOSF, AOSF, POSF, and OSCC was 0.14 ± 0.2, 1.69 ± 1.4, 2.47 ± 1.2, 3.57 ± 2.6, and 8.86 ± 1.4, respectively. All results were statistically significant (P < 0.05).

CONCLUSIONS: The expression of MFs exhibited a gradual increase as the disease progressed from mild to malignant transformation, indicating the contributory role of MFs in the fibrogenesis and potential tumorigenesis associated with OSF.

PMID:38687943 | DOI:10.4103/jcrt.jcrt_498_23

J Cancer Res Ther. 2024 Apr 1;20(2):651-657. doi: 10.4103/jcrt.jcrt_2017_23. Epub 2024 Apr 30.

ABSTRACT

BACKGROUND: The feasibility and safety of enhanced recovery after surgery (ERAS) for percutaneous computed tomography (CT)-guided microwave ablation (MWA) for treating lung nodules remain unclear.

METHODS AND MATERIALS: A total of 409 patients with lung tumors treated at the Department of Thoracic Surgery, First Affiliated Hospital of Guangxi Medical University from August 2020 to May 2023 were enrolled. Perioperative data, including baseline characteristics, operation time, postoperative pain score (visual analog scale [VAS]), hospitalization expenses, postoperative complications, total hospital stay, and patient satisfaction, were observed and recorded.

RESULTS: No perioperative mortality occurred in either group and complete ablation was achieved in all patients. Patients in the ERAS group had significantly shorter hospital stays (P < 0.001), reduced operation times (P = 0.047), lower hospitalization expenses (P < 0.001), lower VAS scores (P < 0.001), and fewer complications (P = 0.047) compared with the traditional group.

CONCLUSIONS: ERAS for percutaneous CT-guided MWA (ERAA) is safe, effective, and feasible for the treatment of lung nodules.

PMID:38687936 | DOI:10.4103/jcrt.jcrt_2017_23

J Cancer Res Ther. 2024 Apr 1;20(2):625-632. doi: 10.4103/jcrt.jcrt_1796_23. Epub 2024 Apr 30.

ABSTRACT

OBJECTIVE: To establish a multimodal model for distinguishing benign and malignant breast lesions.

MATERIALS AND METHODS: Clinical data, mammography, and MRI images (including T2WI, diffusion-weighted images (DWI), apparent diffusion coefficient (ADC), and DCE-MRI images) of 132 benign and breast cancer patients were analyzed retrospectively. The region of interest (ROI) in each image was marked and segmented using MATLAB software. The mammography, T2WI, DWI, ADC, and DCE-MRI models based on the ResNet34 network were trained. Using an integrated learning method, the five models were used as a basic model, and voting methods were used to construct a multimodal model. The dataset was divided into a training set and a prediction set. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated. The diagnostic efficacy of each model was analyzed using a receiver operating characteristic curve (ROC) and an area under the curve (AUC). The diagnostic value was determined by the DeLong test with statistically significant differences set at P < 0.05.

RESULTS: We evaluated the ability of the model to classify benign and malignant tumors using the test set. The AUC values of the multimodal model, mammography model, T2WI model, DWI model, ADC model and DCE-MRI model were 0.943, 0.645, 0.595, 0.905, 0.900, and 0.865, respectively. The diagnostic ability of the multimodal model was significantly higher compared with that of the mammography and T2WI models. However, compared with the DWI, ADC, and DCE-MRI models, there was no significant difference in the diagnostic ability of these models.

CONCLUSION: Our deep learning model based on multimodal image training has practical value for the diagnosis of benign and malignant breast lesions.

PMID:38687933 | DOI:10.4103/jcrt.jcrt_1796_23