Category: Nevin Manimala

Intern Med. 2026 Jul 11. doi: 10.2169/internalmedicine.7638-26. Online ahead of print.

ABSTRACT

Objective To evaluate the needle selection process for endoscopic ultrasound-guided tissue acquisition (EUS-TA) using 22-gauge needles in pancreatic solid lesions ≤20 mm. Methods Consecutive patients with pancreatic solid lesions ≤20 mm who underwent EUS-TA were retrospectively reviewed at a referral center. Only procedures that used 22-gauge needles were included. The overall diagnostic accuracy was compared between the fine-needle aspiration (FNA) and fine-needle biopsy (FNB) groups. Multivariable logistic regression and propensity score-matched analyses were performed. Patients or Materials A total of 348 patients with pancreatic solid lesions ≤20 mm were included (FNA, n=193; FNB, n=155). Results No statistically significant difference in the overall diagnostic accuracy was observed between the FNA and FNB groups (92.7% vs. 95.5%, p=0.27). The median number of needle passes was lower in the FNB group (2 vs. 3, p<0.001), and the histological yield was higher (92.9% vs. 84.5%, p=0.019). The needle type was not independently associated with diagnostic accuracy (OR 1.76, 95% CI 0.69-4.52; p=0.24). In lesions measuring ≤10 mm, subgroup analysis was limited by the small sample size of the study. The rarly adverse event rates were low in both groups. Conclusion In pancreatic solid lesions ≤20 mm, FNB showed a higher histological yield and required fewer needle passes, whereas no significant difference in the diagnostic accuracy was observed between FNA and FNB. Therefore, the routine replacement of FNA with FNB may not be justified when benign-malignant differentiation is sufficient. FNB may be preferable when histological architecture or immunohistochemical evaluation is required and is considered technically feasible.

PMID:42438026 | DOI:10.2169/internalmedicine.7638-26

J Prosthodont Res. 2026 Jul 11. doi: 10.2186/jpr.JPR_D_25_00384. Online ahead of print.

ABSTRACT

PURPOSE: Registration accuracy is vital for digital workflows because misalignment increases errors. This study explores the effects of registration methods, cone-beam computed tomography (CBCT) image quality, and number of registration points (NRP).

METHODS: All participants underwent intraoral and CBCT scanning. In Section 1, registration included four groups: three points, two points and one surface, one point and two surfaces, and three surfaces. In Section 2, after grouping the registrations on the basis of image quality (high, medium, or low), clinicians performed calibration trials. In Section 3, the NRP was increased from two to seven points. Deviations were measured by evaluating the anatomical landmarks (including the cusp tips, central fossae, and ends of the developmental grooves).

RESULTS: Significant differences were observed in the registration methodologies (P < 0.001), with deviations increasing from 257.0 μm (group 3 points) to 479.3 μm (group 3 surfaces). Calibration precision improved proportionally with enhanced CBCT image quality. Deviations significantly decreased from 314.4 μm (low quality) to 237.6 μm (high quality; P < 0.001). Increasing the NRP from two to four points reduced the deviations from 375.4 to 228.2 μm; however, further increasing the NRP beyond four points yielded no significant improvement (P > 0.999, P > 0.999, P = 0.152).

CONCLUSIONS: Clinicians should prioritize point-based registration to improve accuracy. High CBCT image quality significantly improves accuracy. Increasing the NRP beyond four points demonstrates no statistically significant reduction in deviation.

PMID:42438012 | DOI:10.2186/jpr.JPR_D_25_00384

J Vet Med Sci. 2026 Jul 13. doi: 10.1292/jvms.26-0179. Online ahead of print.

ABSTRACT

Mastitis is one of the most common diseases in dairy cows, causing significant economic losses due to reduced productivity and the need for treatment. In recent years, MRSA (methicillin-resistant Staphylococcus aureus) has emerged as an important cause of chronic and recurrent mastitis, posing additional challenges for herd health management. The aim of this study was to conduct a comparative analysis of recurrent mastitis in Holstein-Friesian cows caused by MRSA and non-MRSA Staphylococcus aureus, with a particular focus on the number of recurrences, somatic cell count (SCC), milk production, and risk factors. The study included 120 cows, divided into two groups of 60, raised under identical conditions on a single farm. Milk samples were collected under sterile conditions, cultured on Blood agar and Mannitol Salt agar, and analyzed for the presence of MRSA using PCR for the mecA gene. Cows were monitored for the number of recurrences, SCC, daily milk yield, and response to therapy. Statistical analyses included t-tests, ANOVA, correlation analysis, and logistic regression. The results showed that MRSA-infected cows had significantly higher recurrence rates, along with elevated SCC and reduced milk yield compared to the non-MRSA group. MRSA was identified as the strongest independent risk factor for mastitis recurrence. These findings highlight the importance of regular bacteriological monitoring, appropriate therapy, and preventive measures to reduce economic losses and protect herd health.

PMID:42437984 | DOI:10.1292/jvms.26-0179

Dent Mater J. 2026 Jul 11. doi: 10.4012/dmj.2025-344. Online ahead of print.

ABSTRACT

The aim of this study was to investigate the bond strength of resin composites to the dentin surface after the removal of amalgam restorations. The dentin surfaces of amalgam-removed molars were subjected to different surface treatments (n=45). Following ortho-phosphoric acid, sandblasting, and Er:YAG laser the samples were divided into 3 subgroups, and samples were formed using different universal adhesives (Solare Universal Bond, Gluma Bond Universal, Scotchbond Universal Plus) and nanohybrid composites (G-aenial A’CHORD, Charisma Topaz, Filtek Z550). The microshear bond strength (µSBS) was measured in each group, and the data were statistically analysed via one-way analysis of variance (ANOVA) and Tukey’s post-hoc test. The 3M subgroup (39.91 MPa) had the highest bond strength on the laser-treated dentin surfaces, whereas the lowest value was measured in the Kulzer subgroup (3.61 MPa) on the sandblasted dentin surfaces. Composite resin adhesion to amalgam-contaminated dentin surfaces with surface modifications, especially laser treatment, yielded successful results.

PMID:42437979 | DOI:10.4012/dmj.2025-344

Int J Dent Hyg. 2026 Jul 12. doi: 10.1111/idh.70133. Online ahead of print.

ABSTRACT

BACKGROUND: Oral health is a multi-factorial condition influenced by diet, hygiene and sociocultural habits and its historical evolution can be traced through the bioarchaeological analysis of dento-skeletal remains, offering unique insights into past lifestyles and public health trends.

AIM: This observational study aimed to explore diachronic trends in oral health and hygiene over the last 2000 years in Milan, Italy, through the bioarchaeological analysis of skeletal remains from four historical periods (Roman Era, Early and Late Middle Ages, Modern Era).

METHODS: Forty individuals (equally represented by biological sex and age-at-death) were examined for dental calculus, wear, caries, and bone resorption. Data were collected using standardised clinical indices (OHI, BEWE, ICDAS, Black’s classification and bone resorption) and statistically analysed to identify sex-based and diachronic differences.

RESULTS: A progressive decline in dental calculus and wear was shown, reflecting dietary transitions and improved hygiene practices. Caries prevalence varied across periods, peaking in the Modern Era, while bone resorption varied little across periods. Notable sex-based disparities were observed in the earlier periods, especially regarding calculus, but diminished over time.

CONCLUSIONS: Skeletal remains provide valuable insight into long-term oral health evolution and emphasise the relevance of archaeological evidence for contemporary public health perspectives.

PMID:42437968 | DOI:10.1111/idh.70133

J Health Popul Nutr. 2026 Jul 12. doi: 10.1186/s41043-026-01401-y. Online ahead of print.

ABSTRACT

BACKGROUND: Childhood stunting remains a major public health concern, reflecting chronic undernutrition and long-term socioeconomic disadvantage. Among school-aged children, stunting is associated with impaired physical growth, reduced cognitive development, and poorer educational outcomes. Traditional statistical approaches, such as logistic regression, have been widely used to examine factors associated with stunting; however, their ability to capture complex and nonlinear relationships is limited. Machine learning (ML) methods provide a flexible alternative for modeling such relationships.

OBJECTIVE: This study aimed to model and classify stunting among school-aged children in Ethiopia using school- and household-level data, and to compare the performance of machine learning algorithms with multivariable logistic regression.

METHODS: A cross-sectional analysis was conducted using secondary data from Round 5 (2016-2017) of the Young Lives study in Ethiopia. Stunting was defined as a binary outcome based on World Health Organization height-for-age Z-score criteria. Several machine learning algorithms, including Random Forest, Support Vector Machine, and Gradient Boosting Machine, were implemented. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (AUC). Variable importance measures were used to identify predictors contributing to model performance. To avoid potential circularity, anthropometric variables closely related to the outcome (e.g., child weight) were excluded from the final models.

RESULTS: The Random Forest model demonstrated modestly improved performance compared with logistic regression and other machine learning methods, and its performance was evaluated using accuracy, sensitivity, specificity, AUC, and F1-score. Key predictors included school type, household wealth index, literacy-related indicators, and region of residence. Notable regional variation in stunting classification was observed, suggesting the influence of broader socioeconomic and environmental conditions.

CONCLUSION: Machine learning models, particularly Random Forest, showed slightly better performance than conventional logistic regression in classifying stunting among school-aged children in Ethiopia. The identified predictors highlight the multifactorial and context-dependent nature of stunting. These findings support the use of ML approaches as complementary analytical tools for understanding patterns of child undernutrition, although their application for prediction should be interpreted within the limitations of cross-sectional data.

PMID:42437956 | DOI:10.1186/s41043-026-01401-y

Cardiovasc Diabetol Endocrinol Rep. 2026 Jul 13;12(1):40. doi: 10.1186/s40842-026-00309-0.

ABSTRACT

BACKGROUND: Although a cross-sectional relation between the atherogenic index of plasma (AIP) and gout has been well documented, the causal roles of its principal components, TG and HDL-C, have not yet been clearly established. Therefore, this study sought to explicate the causal relations between AIP, its individual constituents, and the risk of gout, as well as to investigate the potential molecular mechanisms underlying these associations.

METHODS: We adopted an integrated analytical pipeline. First, mediation and cross-sectional analyses were carried out to quantify the phenotypic associations of TG, HDL-C, and AIP with gout. Second, both multivariable and univariable Mendelian randomization (MR) analyses were applied to assess causal directions. Finally, a network pharmacology approach was applied to construct interaction networks linking lipid-related factors with gout, and subsequent enrichment analyses were done to identify the main biological pathways involved and key genes were identified using the Icelandic database’s pQTLs.

RESULTS: AIP showed a significant positive link with gout prevalence (OR = 1.700, p = 0.010), with this relationship being mediated mainly by HDL-C (45.19%), rather than TG or LDL-C. Univariable MR analyses indicated that TG exerted a substantial causal effect on gout risk (OR = 1.0058, p < 0.001). Although univariable MR showed a nominally protective association for HDLC (OR = 0.623, 95% CI: 0.399-0.974, p = 0.042), this effect was attenuated and became statistically nonsignificant after multivariable adjustment for other lipid traits (p > 0.05). After adjustment for genetic correlations in multivariable MR analyses, TG remained the sole lipid trait with a robust and independent causal effect on gout (OR = 1.0077, p < 0.001), whereas neither HDL-C nor LDL-C reached statistical significance. The TG-specific gout-associated gene set (Group B) was significantly enriched in T-cell receptor and NF-κB signaling pathways, with hub genes including PTPRC, MYD88, and LCK, supporting a direct lipid-immune axis. By contrast, the combined TG/HDL-C gene set (Group F) showed predominant enrichment in PI3K-Akt and cytokine-cytokine receptor pathways, whereas genes uniquely shared between HDL-C and gout (Group C) were mainly enriched in fundamental cellular processes without marked inflammatory pathway involvement.

CONCLUSIONS: AIP exhibits a non-linear positive association with gout, but this association is primarily mediated by HDL‑C, which itself has no independent causal protective effect. TG may play an independent causal role in gout, potentially involving T‑cell receptor/NF‑κB signaling pathways. However, these findings are exploratory and require further validation.

PMID:42437949 | DOI:10.1186/s40842-026-00309-0

Reprod Biol Endocrinol. 2026 Jul 13;24(1):69. doi: 10.1186/s12958-026-01578-1.

ABSTRACT

BACKGROUND: Donated eggs are essential for a range of Medically Assisted Reproduction (MAR) procedures, but relatively little has been written about how donors are recruited. This study examined egg donor recruitment processes at a commercial egg-bank in Florida (USA). It documents what proportion of applicants were ultimately accepted and, if rejected, at what step, whether this was by choice or selection, whether their initial ID-release choice was important, and how this compares to the recruitment of sperm donors.

METHODS: Anonymised records of all egg donor applicants in 2018 and 2019 (n = 2,443) were examined to determine the number passing through (or lost) at each stage of the recruitment process and ultimately how many had successful egg retrieval and cryopreservation. Statistical analysis was carried out to examine differences between the initial ID-release choice made by egg donor applicants (ID-release vs. non-ID release).

RESULTS: Few applicants (2.5%) were accepted and had eggs frozen for donation. This did not differ between applicants who opted at the outset to be ID-release (2.94%) compared to those who didn’t (2.12%) (X₂ = 1.682; Df = 1; Z = 1.297; p = 0.1947). Most were lost during recruitment because they: (i) did not meet the eligibility criteria at the outset (51.17%); (ii) withdrew, failed to respond, did not attend an appointment, or did not return a questionnaire (26.36%); or (iii) reported a disqualifying health issue or failed a screening test (19.69%). There were no significant differences between the initial ID choice of egg donor candidates and the reason for their loss from the process. This differed from what we know about sperm donor recruitment during the same period at the same clinic. Only two women who were accepted to donate failed to do so because of a poor ovarian response. During recruitment, some egg donors decided to change ID-type and it was more common for them to change from non-ID release to ID release (53.57%) than the other way around (9.09%) (X₂ = 14.920; Df = 1; Z = 3.863; p < 0.0001).

CONCLUSION: This study demonstrates how challenging egg donor recruitment processes are, with only a small fraction of those who initially apply ultimately being accepted and having samples certified as safe for use in treatment. The initial ID-release choice of egg donor applicants in the USA has no bearing on whether they were finally accepted as donors or not and is unrelated to their reason for rejection from the program.

PMID:42437948 | DOI:10.1186/s12958-026-01578-1

BMC Psychol. 2026 Jul 13. doi: 10.1186/s40359-026-05176-z. Online ahead of print.

ABSTRACT

BACKGROUND: This study aimed to compare antisocial, narcissistic, and Machiavellian traits, along with attention-deficit/hyperactivity disorder (ADHD) symptoms and psychiatric history, between combat athletes and non-athletes.

METHOD: In this cross-sectional study, a total of 300 participants (150 combat athletes and 150 non-athletic controls) completed online assessments. The Sociodemographic and Clinical Data Form, Adult ADHD Self-Report Scale, Narcissistic Personality Inventory, Antisocial Behaviour Scale, and Machiavellianism Scale were administered. Group comparisons were performed using appropriate statistical tests.

RESULTS: Combat athletes demonstrated significantly higher scores on the Antisocial Behaviour Scale (27.4 ± 6.3 vs. 21.1 ± 5.9; p < 0.001), Narcissistic Personality Inventory (17.6 ± 4.8 vs. 13.2 ± 4.5; p < 0.001), and Machiavellianism Scale (41.3 ± 7.1 vs. 36.0 ± 6.4; p < 0.001). ADHD symptom scores were descriptively higher in combat athletes (31.9 ± 9.2 vs. 25.5 ± 8.6; p < 0.001); however, ADHD symptoms did not remain an independent predictor in the multivariable model. In addition, psychiatric diagnoses, medication use, self-harm behaviors, and suicide attempts were more prevalent among combat athletes.

CONCLUSION: Individuals engaged in combat sports exhibited higher levels of antisocial, narcissistic, and Machiavellian traits, along with descriptively elevated ADHD symptomatology and psychiatric vulnerability. However, ADHD symptoms did not show an independent predictive signal in adjusted models, whereas narcissistic traits emerged as the most robust independent correlate of combat sports participation. These findings highlight the importance of targeted mental health screening and intervention strategies in this population.

PMID:42437937 | DOI:10.1186/s40359-026-05176-z

BMC Res Notes. 2026 Jul 12. doi: 10.1186/s13104-026-07888-2. Online ahead of print.

ABSTRACT

OBJECTIVES: To describe the steps taken in the creation of a Data Pack containing clinical data from the FOCUS4 randomized controlled trial in colorectal cancer, representing a case study of clinical trial data access and sharing.

DATA DESCRIPTION: The FOCUS4 trial recruited 1434 metastatic colorectal cancer patients between January 2017 and March 2020 from 94 sites across the United Kingdom (UK). The trial used an adaptive “umbrella” design, in which tumour samples from recruited patients underwent molecular testing, the results of which determined into which of multiple parallel randomized allocations they would be eligible to enter. Overall, 361 patients were successfully randomised into one of four “sub-trials”: FOCUS4-B (6 patients), FOCUS4-C (67 patients), FOCUS4-D (32 patients) and FOCUS4-N (254 patients). After the trial had concluded, we created a Data Pack containing pseudonymised data organised into registration data and follow up data, marked case report forms (CRFs), data dictionary, study protocols and statistical analysis plan. This will help researchers to easily understand the datasets for study replication, further research analysis or to integrate with other existing datasets or repositories.

PMID:42437936 | DOI:10.1186/s13104-026-07888-2