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Fulltext Iptacopan monotherapy in patients with paroxysmal nocturnal hemoglobinuria: a 2-cohort open-label proof-of-concept study

Jang JH, Wong L, Ko BS, Yoon SS, Li K, Baltcheva I, et al.

Blood Adv, 2022 08 09;6(15):4450-4460.
PMID: 35561315 DOI: 10.1182/bloodadvances.2022006960

Iptacopan (LNP023) is a novel, oral selective inhibitor of complement factor B under clinical development for paroxysmal nocturnal hemoglobinuria (PNH). In this ongoing open-label phase 2 study, PNH patients with active hemolysis were randomized to receive single-agent iptacopan twice daily at a dose of either 25 mg for 4 weeks followed by 100 mg for up to 2 years (cohort 1) or 50 mg for 4 weeks followed by 200 mg for up to 2 years (cohort 2). At the time of interim analysis, of 13 PNH patients enrolled, all 12 evaluable for efficacy achieved the primary endpoint of reduction in serum lactate dehydrogenase (LDH) levels by ≥60% by week 12 compared with baseline; mean LDH levels dropped rapidly and durably, namely by 77% and 85% at week 2 and by 86% and 86% at week 12 in cohorts 1 and 2, respectively. Most patients achieved a clinically meaningful improvement in hemoglobin (Hb) levels, and all but 1 patient remained transfusion-free up to week 12. Other markers of hemolysis, including bilirubin, reticulocytes, and haptoglobin, showed consistent improvements. No thromboembolic events were reported, and iptacopan was well tolerated, with no severe or serious adverse events reported until the data cutoff. In addition to the previously reported beneficial effect of iptacopan add-on therapy to eculizumab, this study showed that iptacopan monotherapy in treatment-naïve PNH patients resulted in normalization of hemolytic markers and rapid transfusion-free improvement of Hb levels in most patients. This trial was registered at www.clinicaltrials.gov as #NCT03896152.
Factors influencing degree of hearing loss at presentation, hearing aid choice, and usage in first time hearing aid users in Singapore

Ho EC, Ong WMW, Li K, Zhang H, Bei YTE, Medapati SVR, et al.

Int J Audiol, 2018 10;57(10):776-783.
PMID: 29957077 DOI: 10.1080/14992027.2018.1476781

OBJECTIVE: To examine the factors associated with late presentation at first hearing aid (HA) fitting, HA choice and usage among users in Singapore.
DESIGN: Retrospective cross-sectional study.
STUDY SAMPLE: 1068 subjects issued with HAs at a tertiary hospital from 2001 to 2013.
RESULTS: Half of the subjects presented with more severe (>55 dB) hearing loss (HL) in their better ear. In multivariable analysis, older age, Malay ethnicity, conductive and mixed HL, and combination type of HL were associated with more severe HL at first presentation. Over 70% of subjects were older than 65 years. Worse pure tone audiometry (PTA) thresholds of the better ear, gradual onset and sensorineural HL were associated with older age presentation. For unilaterally fitted subjects, PTA thresholds were the only determinant of having the better ear aided. Better PTA thresholds, younger age and sensorineural HL were associated with choosing in ear compared to behind the ear HAs. Younger age and worse PTA of the better ear were associated with ≥4 h of daily HA usage.
CONCLUSIONS: Age, ethnicity and type of HL were important determinants for more severe HL at first HA fitting. Older patients and those with better hearing were less likely to use their HAs regularly.
Intake of coffee, decaffeinated coffee, or tea does not affect risk for pancreatic cancer: results from the European Prospective Investigation into Nutrition and Cancer Study

Bhoo-Pathy N, Uiterwaal CS, Dik VK, Jeurnink SM, Bech BH, Overvad K, et al.

Clin Gastroenterol Hepatol, 2013 Nov;11(11):1486-92.
PMID: 23756220 DOI: 10.1016/j.cgh.2013.05.029

BACKGROUND & AIMS: Few modifiable risk factors have been implicated in the etiology of pancreatic cancer. There is little evidence for the effects of caffeinated coffee, decaffeinated coffee, or tea intake on risk of pancreatic cancer. We investigated the association of total coffee, caffeinated coffee, decaffeinated coffee, and tea consumption with risk of pancreatic cancer.
METHODS: This study was conducted within the European Prospective Investigation into Nutrition and Cancer cohort, comprising male and female participants from 10 European countries. Between 1992 and 2000, there were 477,312 participants without cancer who completed a dietary questionnaire and were followed up to determine pancreatic cancer incidence. Coffee and tea intake was calibrated with a 24-hour dietary recall. Adjusted hazard ratios (HRs) were computed using multivariable Cox regression.
RESULTS: During a mean follow-up period of 11.6 y, 865 first incidences of pancreatic cancers were reported. When divided into fourths, neither total intake of coffee (HR, 1.03; 95% confidence interval [CI], 0.83-1.27; high vs low intake), decaffeinated coffee (HR, 1.12; 95% CI, 0.76-1.63; high vs low intake), nor tea were associated with risk of pancreatic cancer (HR, 1.22, 95% CI, 0.95-1.56; high vs low intake). Moderately low intake of caffeinated coffee was associated with an increased risk of pancreatic cancer (HR, 1.33; 95% CI, 1.02-1.74), compared with low intake. However, no graded dose response was observed, and the association attenuated after restriction to histologically confirmed pancreatic cancers.
CONCLUSIONS: Based on an analysis of data from the European Prospective Investigation into Nutrition and Cancer cohort, total coffee, decaffeinated coffee, and tea consumption are not related to the risk of pancreatic cancer.
Automatic slice thickness measurement on three types of Catphan CT phantoms

Anam C, Naufal A, Sutanto H, Arifin Z, Hidayanto E, Tan LK, et al.

Biomed Phys Eng Express, 2023 May 30;9(4).
PMID: 37216929 DOI: 10.1088/2057-1976/acd785

Objective. To develop an algorithm to measure slice thickness running on three types of Catphan phantoms with the ability to adapt to any misalignment and rotation of the phantoms.Method. Images of Catphan 500, 504, and 604 phantoms were examined. In addition, images with various slice thicknesses ranging from 1.5 to 10.0 mm, distance to the iso-center and phantom rotations were also examined. The automatic slice thickness algorithm was carried out by processing only objects within a circle having a diameter of half the diameter of the phantom. A segmentation was performed within an inner circle with dynamic thresholds to produce binary images with wire and bead objects within it. Region properties were used to distinguish wire ramps and bead objects. At each identified wire ramp, the angle was detected using the Hough transform. Profile lines were then placed on each ramp based on the centroid coordinates and detected angles, and the full-width at half maximum (FWHM) was determined for the average profile. The slice thickness was obtained by multiplying the FWHM by the tangent of the ramp angle (23°).Results. Automatic measurements work well and have only a small difference (<0.5 mm) from manual measurements. For slice thickness variation, automatic measurement successfully performs segmentation and correctly locates the profile line on all wire ramps. The results show measured slice thicknesses that are close (<3 mm) to the nominal thickness at thin slices, but slightly deviated for thicker slices. There is a strong correlation (R2= 0.873) between automatic and manual measurements. Testing the algorithm at various distances from the iso-center and phantom rotation angle also produced accurate results.Conclusion. An automated algorithm for measuring slice thickness on three types of Catphan CT phantom images has been developed. The algorithm works well on various thicknesses, distances from the iso-center, and phantom rotations.
Fulltext Machine learning based predictive model of Type 2 diabetes complications using Malaysian National Diabetes Registry: A study protocol

Abas MZ, Li K, Hairi NN, Choo WY, Wan KS

J Public Health Res, 2024 Jan;13(1):22799036241231786.
PMID: 38434578 DOI: 10.1177/22799036241231786

BACKGROUND: The prevalence of diabetes in Malaysia is increasing, and identifying patients with higher risk of complications is crucial for effective management. The use of machine learning (ML) to develop prediction models has been shown to outperform non-ML models. This study aims to develop predictive models for Type 2 Diabetes (T2D) complications in Malaysia using ML techniques.
DESIGN AND METHODS: This 10-year retrospective cohort study uses clinical audit datasets from Malaysian National Diabetes Registry from 2011 to 2021. T2D patients who received treatment in public health clinics in the southern region of Malaysia with at least two data points in 10 years are included. Patients with diabetes complications at baseline are excluded to ensure temporality between predictors and the target variable. Appropriate methods are used to address issues related to data cleaning, missing data imputation, data splitting, feature selection, and class imbalance. The study uses 7 ML algorithms, including logistic regression, support vector machine, k-nearest neighbours, decision tree, random forest, extreme gradient boosting, and light gradient boosting machine, to develop predictive models for four target variables: nephropathy, retinopathy, ischaemic heart disease, and stroke. Hyperparameter tuning is performed for each algorithm. The model training is performed using a stratified k-fold cross-validation technique. The best model for each algorithm is evaluated on a hold-out dataset using multiple metrics.
EXPECTED IMPACT OF THE STUDY ON PUBLIC HEALTH: The prediction model may be a valuable tool for diabetes management and secondary prevention by enabling earlier interventions and optimal resource allocation, leading to better health outcomes.
Evidence for the Higgs Boson Decay to a Z Boson and a Photon at the LHC

Aad G, Abbott B, Abeling K, Abicht NJ, Abidi SH, Aboulhorma A, et al.

Phys Rev Lett, 2024 Jan 12;132(2):021803.
PMID: 38277607 DOI: 10.1103/PhysRevLett.132.021803

The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.