Temporal variations in meibomian gland structure—A pilot study

Purpose: To investigate whether there is a measurable change in meibomian gland morphological characteristics over the course of a day (12h) and over a month.Methods: The study enrolled 15 participants who attended a total of 11 study visits spanning a 5-week period. To assess diurnal changes in meibomian glands, seven visits were conducted on a single day, each 2h apart. For monthly assessment, participants attended an additional visit at the same time of the day every week for three consecutive weeks. Meibography using the LipiView® II system was performed at each visit, and meibomian gland morphological parameters were calculated using custom semi-automated software. Specifically, six central glands were analysed for gland length ratio, gland width, gland area, gland intensity and gland tortuosity.Results: The average meibomian gland morphological metrics did not exhibit significant changes during the course of a day or over a month. Nonetheless, certain individual gland metrics demonstrated notable variation over time, both diurnally and monthly. Specifically, meibomian gland length ratio, area, width and tortuosity exhibited significant changes both diurnally and monthly when assessed on a gland-by-gland basis.Conclusions: Meibomian glands demonstrated measurable structural change over short periods of time (hours and days). These results have implications for innovation in gland imaging and for developing precision monitoring of gland structure to assess meibomian gland  health more accurately

Reporting quality of studies using machine learning models for medical diagnosis: a systematic review

Aims: We conducted a systematic review assessing the reporting quality of studies validating models based on machine learning (ML) for clinical diagnosis, with a specific focus on the reporting of information concerning the participants on which the diagnostic task was evaluated on. Method: Medline Core Clinical Journals were searched for studies published between July 2015 and July 2018. Two reviewers independently screened the retrieved articles, a third reviewer resolved any discrepancies. An extraction list was developed from the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guideline. Two reviewers independently extracted the data from the eligible articles. Third and fourth reviewers checked, verified the extracted data as well as resolved any discrepancies between the reviewers Results: The search results yielded 161 papers, of which 28 conformed to the eligibility criteria. Detail of data source was reported in 24 of the 28 papers. For all of the papers, the set of patients on which the ML-based diagnostic system was evaluated was partitioned from a larger dataset, and the method for deriving such set was always reported. Information on the diagnostic/non-diagnostic classification was reported well (23/28). The least reported items were the use of reporting guideline (0/28), distribution of disease severity (8/28 patient flow diagram (10/28) and distribution of alternative diagnosis (10/28). A large proportion of studies (23/28) had a delay between the conduct of the reference standard and ML tests, while one study did not and four studies were unclear. For 15 studies, it was unclear whether the evaluation group corresponded to the setting in which the ML test will be applied to. Conclusion: All studies in this review failed to use reporting guidelines, and a large proportion of them lacked adequate detail on participants, making it difficult to replicate, assess and interpret study findings

The impact of obesity and bariatric surgery on the immune microenvironment of the endometrium

BACKGROUND: The incidence of endometrial cancer is rising in parallel with the obesity epidemic. Obesity increases endometrial cancer risk and weight loss is protective, but the underlying mechanisms are incompletely understood. We hypothesise that the immune microenvironment may influence susceptibility to malignant transformation in the endometrium. The aim of this study was to measure the impact of obesity and weight loss on the immunological landscape of the endometrium. METHODS: We conducted a prospective cohort study of women with class III obesity (body mass index, BMI ≥ 40 kg/m(2)) undergoing bariatric surgery or medically-supervised low-calorie diet. We collected blood and endometrial samples at baseline, and two and 12 months after weight loss intervention. Serum was analysed for inflammatory markers CRP, IL-6 and TNF-α. Multiplex immunofluorescence was used to simultaneously identify cells positive for immune markers CD68, CD56, CD3, CD8, FOXP3 and PD-1 in formalin-fixed paraffin-embedded endometrial tissue sections. Kruskal–Wallis tests were used to determine whether changes in inflammatory and immune biomarkers were associated with weight loss. RESULTS: Forty-three women with matched serum and tissue samples at all three time points were included in the analysis. Their median age and BMI were 44 years and 52 kg/m(2), respectively. Weight loss at 12 months was greater in women who received bariatric surgery (n = 37, median 63.3 kg) than low-calorie diet (n = 6, median 12.8 kg). There were significant reductions in serum CRP (p = 3.62 × 10(−6), r = 0.570) and IL-6 (p = 0.0003, r = 0.459), but not TNF-α levels, with weight loss. Tissue immune cell densities were unchanged except for CD8+ cells, which increased significantly with weight loss (p = 0.0097, r = −0.323). Tissue CD3+ cell density correlated negatively with systemic IL-6 levels (p = 0.0376; r = −0.318). CONCLUSION: Weight loss is associated with reduced systemic inflammation and a recruitment of protective immune cell types to the endometrium, supporting the concept that immune surveillance may play a role in endometrial cancer prevention

Immune infiltrate diversity confers a good prognosis in follicular lymphoma

From Springer Nature via Jisc Publications RouterHistory: received 2020-08-25, accepted 2021-04-14, registration 2021-04-15, pub-electronic 2021-04-30, online 2021-04-30, pub-print 2021-12Publication status: PublishedFunder: Manchester Biomedical Research Centre; doi: http://dx.doi.org/10.13039/100014653; Grant(s): IS-BRC-1215–20007Funder: Manchester Cancer Research CentreAbstract: Background: Follicular lymphoma (FL) prognosis is influenced by the composition of the tumour microenvironment. We tested an automated approach to quantitatively assess the phenotypic and spatial immune infiltrate diversity as a prognostic biomarker for FL patients. Methods: Diagnostic biopsies were collected from 127 FL patients initially treated with rituximab-based therapy (52%), radiotherapy (28%), or active surveillance (20%). Tissue microarrays were constructed and stained using multiplex immunofluorescence (CD4, CD8, FOXP3, CD21, PD-1, CD68, and DAPI). Subsequently, sections underwent automated cell scoring and analysis of spatial interactions, defined as cells co-occurring within 30 μm. Shannon’s entropy, a metric describing species biodiversity in ecological habitats, was applied to quantify immune infiltrate diversity of cell types and spatial interactions. Immune infiltrate diversity indices were tested in multivariable Cox regression and Kaplan–Meier analysis for overall (OS) and progression-free survival (PFS). Results: Increased diversity of cell types (HR = 0.19 95% CI 0.06–0.65, p = 0.008) and cell spatial interactions (HR = 0.39, 95% CI 0.20–0.75, p = 0.005) was associated with favourable OS, independent of the Follicular Lymphoma International Prognostic Index. In the rituximab-treated subset, the favourable trend between diversity and PFS did not reach statistical significance. Conclusion: Multiplex immunofluorescence and Shannon’s entropy can objectively quantify immune infiltrate diversity and generate prognostic information in FL. This automated approach warrants validation in additional FL cohorts, and its applicability as a pre-treatment biomarker to identify high-risk patients should be further explored. The multiplex image dataset generated by this study is shared publicly to encourage further research on the FL microenvironment